Data in Table 1 is from House of Commons Library reports (Audickas, Hawkins, & Cracknell, 2017; Kelly, 2016). All women short lists were not used by Labour during the 2001 General Election.
| General Election | Total MPs | Labour MPs | Female Labour MPs | Labour MPs Intake | Intake Women | Intake Short list | Nominated Short list |
|---|---|---|---|---|---|---|---|
| 1997 | 659 | 418 | 101 (24%) | 177 | 64 (36%) | 35 | 38 |
| 2001 | 659 | 412 | 95 (23%) | 38 | 4 (11%) | 0 | 0 |
| 2005 | 646 | 355 | 98 (28%) | 40 | 26 (65%) | 23 | 30 |
| 2010 | 650 | 258 | 81 (31%) | 64 | 32 (50%) | 28 | 63 |
| 2015 | 650 | 232 | 99 (43%) | 49 | 31 (63%) | 31 | 77 |
| Gender | Speeches | Words |
|---|---|---|
| All | 656,412 | 111,180,398 |
| Female | 148,702 | 26,231,034 |
| Male | 507,710 | 84,949,364 |
| Conservatives | ||
| All | 285,291 | 44,800,169 |
| Female | 48,768 | 7,363,031 |
| Male | 236,523 | 37,437,138 |
| Labour | ||
| All | 261,942 | 46,494,850 |
| Female | 84,569 | 15,897,929 |
| Non-All Women Shortlists | 28,695 | 5,422,776 |
| All Women Shortlists | 55,874 | 10,475,153 |
| Male | 177,373 | 30,596,921 |
| Liberal Democrat | ||
| All | 72,716 | 13,485,902 |
| Female | 7,552 | 1,503,459 |
| Male | 65,164 | 11,982,443 |
| Other | ||
| All | 36,463 | 6,399,477 |
| Female | 7,813 | 1,466,615 |
| Male | 28,650 | 4,932,862 |
Previous research on gender differences in political speech patterns has focused on differences between male and female politicians (Yu, 2014) or on variations in Hilary Clinton’s speech patterns (Bligh, Merolla, Schroedel, & Gonzalez, 2010; Jones, 2016). This paper focuses on differences in speech patterns between female Labour MPs nominated through All Women Shortlists (AWS) and female Labour MPs nominated through open short lists. We examined differences in speaking styles using the Linguistic Inquiry and Word Count 2015 (LIWC) dictionary (Pennebaker, Boyd, Jordan, & Blackburn, 2015) and the spaCy (Honnibal & Montani, 2017) Parts-of-Speech (POS) tagger. We examined differences in the topics discussed by AWS and non-AWS MPs, using \({\chi}^2\) tests for individual words and for bigrams. We trained a Naive Bayes classifier to distinguish AWS and non-AWS speeches. We used structured topic models (STM) to identify the topics discussed by AWS and non-AWS MPs.
To account for the possible effects of age, parliamentary experience and cohort, and in order to compare women selected through all women short lists to women who were not (but who theoretically had the opportunity to contest all-women short lists), our analysis is been restricted only to Labour MPs first elected to the House of Commons in the 1997 General Election, up to but excluding the 2017 General Election. Comparisons between MPs of different parties are also restricted to MPs first elected in the 1997 General Election, and before the 2017 General Election. Speeches made by the Speaker, including Deputy Speakers, were also excluded. Words contained in parentheses were removed, as they are added by Hansard to provide additional information not actually spoken by the MP.1 Speeches and data on MPs’ gender and party affiliation are from a previously assembled dataset (Odell, 2018). Information on candidates selected through all women short lists is from the House of Commons Library (Kelly, 2016). Unsuccessful General Election candidates selected through all women short lists who were subsequently elected in a byelection are classified as having been selected on an all women short list.
Word classification used the Linguistic Inquiry and Word Count 2015 (LIWC) dictionary (Pennebaker et al., 2015) and tokenising tools from the Quanteda R package (Benoit, 2018). Word counts and words-per-sentence were calculated using stringi (Gagolewski, 2018), a wrapper to the ICU regex library.
Following Yu (2014) drawing on Newman, Groom, Handelman, & Pennebaker (2008) we used the following LIWC categories:
We also included mean words-per-sentence (WPS), total speach word count (WC) and Flesch–Kincaid grade level (FK) (Kincaid, Fishburne, Rogers, & Chissom, 1975), calculated using Quanteda (Benoit, 2018) and stringi (Gagolewski, 2018).
| Mean | SD | Mean | SD | Cohen’s D | Magnitude | |
|---|---|---|---|---|---|---|
| All Pronouns | 10.07 | 4.60 | 10.15 | 4.99 | 0.02 | negligible |
| First person singular pronouns | 1.89 | 2.41 | 2.02 | 2.55 | 0.05 | negligible |
| First person plural pronouns | 0.97 | 1.42 | 0.99 | 1.51 | 0.01 | negligible |
| Verbs | 12.82 | 5.00 | 12.67 | 5.36 | -0.03 | negligible |
| Auxiliary verbs | 7.91 | 3.45 | 7.93 | 3.69 | 0.01 | negligible |
| Social processes | 8.47 | 4.82 | 8.18 | 5.11 | -0.06 | negligible |
| Positive emotions | 2.73 | 2.49 | 2.57 | 2.54 | -0.06 | negligible |
| Negative emotions | 1.15 | 1.68 | 1.07 | 1.77 | -0.05 | negligible |
| Tentative words | 1.48 | 1.74 | 1.58 | 1.90 | 0.05 | negligible |
| More than six letters | 10.58 | 3.68 | 10.22 | 3.92 | -0.10 | negligible |
| Articles | 7.65 | 3.30 | 7.96 | 3.55 | 0.09 | negligible |
| Prepositions | 12.58 | 4.42 | 12.14 | 4.73 | -0.10 | negligible |
| Anger words | 0.23 | 0.81 | 0.24 | 0.77 | 0.01 | negligible |
| Swear words | 0.00 | 0.06 | 0.00 | 0.09 | 0.01 | negligible |
| Cognitive processes | 8.68 | 4.83 | 8.82 | 5.15 | 0.03 | negligible |
| Words per Sentence | 43.63 | 19.68 | 41.15 | 20.04 | -0.13 | negligible |
| Total Word Count | 402.79 | 691.27 | 370.18 | 647.36 | -0.05 | negligible |
| Flesh-Kincaid Grade Level | 10.81 | 7.68 | 9.78 | 7.87 | -0.13 | negligible |
There are no categories where gender differences meet the effect size threshold of \(|0.2|\) suggested by Cohen (1988, pp. 25–26) to indicate a small effect. 4 categories – words with more than six letters, prepositions, words-per-sentence and Flesh-Kincaid grade level – exceeded the \(|0.1|\) threshold suggested by Newman et al (2008).
The following plots show changes in the occurences of selected LIWC terms, words-per-sentence, total word count and Flesch–Kincaid grade level, over the course of an MP’s career. There do not appear to be any notable changes in speaking style over the course of female Labour MPs’ careers.
Occurence of selected LIWC terms
| Mean | SD | Mean | SD | Cohen’s D | Magnitude | |
|---|---|---|---|---|---|---|
| All Pronouns | 10.01 | 4.67 | 10.19 | 4.48 | -0.04 | negligible |
| First person singular pronouns | 1.86 | 2.41 | 1.95 | 2.42 | -0.04 | negligible |
| First person plural pronouns | 0.88 | 1.36 | 1.16 | 1.51 | -0.19 | negligible |
| Verbs | 12.88 | 5.10 | 12.69 | 4.80 | 0.04 | negligible |
| Auxiliary verbs | 7.94 | 3.49 | 7.86 | 3.38 | 0.02 | negligible |
| Social processes | 8.48 | 4.94 | 8.46 | 4.59 | 0.00 | negligible |
| Positive emotions | 2.69 | 2.52 | 2.81 | 2.42 | -0.05 | negligible |
| Negative emotions | 1.16 | 1.69 | 1.13 | 1.67 | 0.02 | negligible |
| Tentative words | 1.48 | 1.75 | 1.49 | 1.73 | 0.00 | negligible |
| More than six letters | 10.52 | 3.73 | 10.70 | 3.58 | -0.05 | negligible |
| Articles | 7.69 | 3.38 | 7.55 | 3.15 | 0.04 | negligible |
| Prepositions | 12.55 | 4.54 | 12.63 | 4.15 | -0.02 | negligible |
| Anger words | 0.23 | 0.78 | 0.24 | 0.88 | -0.01 | negligible |
| Swear words | 0.00 | 0.06 | 0.00 | 0.05 | 0.01 | negligible |
| Cognitive processes | 8.59 | 4.90 | 8.86 | 4.69 | -0.06 | negligible |
| Words per Sentence | 44.02 | 20.45 | 42.85 | 18.05 | 0.06 | negligible |
| Total Word Count | 401.77 | 704.40 | 404.78 | 664.97 | 0.00 | negligible |
| Flesh-Kincaid Grade Level | 10.97 | 7.97 | 10.48 | 7.06 | 0.07 | negligible |
There are no categories among female Labour MPs by selection process meeting the \(|0.2|\) threshold. Only one category – first person plural pronouns, d=0.19 – exceeded \(|0.1|\).
| Mean | SD | Mean | SD | Cohen’s D | Magnitude | |
|---|---|---|---|---|---|---|
| All Pronouns | 10.12 | 4.87 | 10.62 | 4.84 | 0.10 | negligible |
| First person singular pronouns | 1.98 | 2.51 | 2.15 | 2.56 | 0.07 | negligible |
| First person plural pronouns | 0.98 | 1.48 | 1.22 | 1.70 | 0.15 | negligible |
| Verbs | 12.72 | 5.24 | 12.93 | 5.13 | 0.04 | negligible |
| Auxiliary verbs | 7.92 | 3.61 | 8.17 | 3.58 | 0.07 | negligible |
| Social processes | 8.28 | 5.02 | 8.13 | 4.80 | -0.03 | negligible |
| Positive emotions | 2.62 | 2.53 | 2.85 | 2.66 | 0.09 | negligible |
| Negative emotions | 1.10 | 1.74 | 1.05 | 1.78 | -0.03 | negligible |
| Tentative words | 1.55 | 1.85 | 1.57 | 1.88 | 0.01 | negligible |
| More than six letters | 10.34 | 3.85 | 10.28 | 3.76 | -0.02 | negligible |
| Articles | 7.86 | 3.48 | 7.82 | 3.45 | -0.01 | negligible |
| Prepositions | 12.28 | 4.64 | 12.38 | 4.49 | 0.02 | negligible |
| Anger words | 0.24 | 0.78 | 0.24 | 0.82 | 0.01 | negligible |
| Swear words | 0.00 | 0.08 | 0.00 | 0.10 | 0.00 | negligible |
| Cognitive processes | 8.77 | 5.05 | 8.86 | 5.06 | 0.02 | negligible |
| Words per Sentence | 41.95 | 19.96 | 42.76 | 20.16 | 0.04 | negligible |
| Total Word Count | 380.71 | 662.03 | 335.54 | 592.41 | -0.07 | negligible |
| Flesh-Kincaid Grade Level | 10.12 | 7.82 | 10.41 | 7.91 | 0.04 | negligible |
There are no categories with effect sizes exceeding \(|0.2|\) between Labour and Conservative MPs, like inter-Labour differences.
There are no categories with effect sizes exceeding \(|0.2|\) when comparing all male and female MPs elected from 1997 onwards. There is only one category, “Articles”, with an effect size of 0.11, greater than the \(|0.1|\) threshold suggested by Newman et al. (2008).
| Mean | SD | Mean | SD | Cohen’s D | Magnitude | |
|---|---|---|---|---|---|---|
| All Pronouns | 10.31 | 4.65 | 10.26 | 4.90 | -0.01 | negligible |
| First person singular pronouns | 1.99 | 2.45 | 2.00 | 2.52 | 0.00 | negligible |
| First person plural pronouns | 1.11 | 1.57 | 1.08 | 1.59 | -0.02 | negligible |
| Verbs | 12.88 | 4.97 | 12.80 | 5.26 | -0.02 | negligible |
| Auxiliary verbs | 8.00 | 3.45 | 8.08 | 3.64 | 0.02 | negligible |
| Social processes | 8.45 | 4.77 | 8.00 | 4.93 | -0.09 | negligible |
| Positive emotions | 2.84 | 2.53 | 2.69 | 2.58 | -0.06 | negligible |
| Negative emotions | 1.10 | 1.65 | 1.08 | 1.78 | -0.01 | negligible |
| Tentative words | 1.47 | 1.73 | 1.61 | 1.91 | 0.08 | negligible |
| More than six letters | 19.73 | 6.94 | 19.25 | 7.18 | -0.07 | negligible |
| Articles | 7.62 | 3.31 | 8.00 | 3.51 | 0.11 | negligible |
| Prepositions | 12.58 | 4.36 | 12.22 | 4.62 | -0.08 | negligible |
| Anger words | 0.23 | 0.78 | 0.25 | 0.82 | 0.02 | negligible |
| Swear words | 0.00 | 0.05 | 0.00 | 0.10 | 0.01 | negligible |
| Cognitive processes | 8.67 | 4.79 | 8.93 | 5.12 | 0.05 | negligible |
| Words per Sentence | 43.25 | 19.45 | 42.06 | 20.12 | -0.06 | negligible |
| Total Word Count | 377.31 | 648.92 | 358.13 | 623.49 | -0.03 | negligible |
| Flesh-Kincaid Grade Level | 10.63 | 7.61 | 10.16 | 7.89 | -0.06 | negligible |
| Word Type | Mean | SD | Mean | SD | Cohen’s D | Magnitude |
|---|---|---|---|---|---|---|
| All Nouns | 22.18 | 9.60 | 21.66 | 10.96 | -0.05 | negligible |
| Plural Nouns | 5.85 | 3.72 | 5.03 | 3.79 | -0.22 | small |
| Singular Nouns | 15.62 | 9.84 | 16.01 | 11.19 | 0.04 | negligible |
| Adjectives | 9.58 | 4.78 | 9.28 | 5.29 | -0.06 | negligible |
| Adverbs | 4.91 | 4.26 | 5.07 | 4.91 | 0.03 | negligible |
| Verbs | 20.94 | 9.52 | 20.78 | 10.28 | -0.02 | negligible |
| Word Type | Mean | SD | Mean | SD | Cohen’s D | Magnitude |
|---|---|---|---|---|---|---|
| All Nouns | 22.16 | 8.78 | 22.18 | 10.00 | -0.04 | negligible |
| Plural Nouns | 6.03 | 3.60 | 5.76 | 3.77 | -0.16 | negligible |
| Singular Nouns | 15.51 | 8.97 | 15.67 | 10.26 | 0.02 | negligible |
| Adjectives | 9.83 | 4.59 | 9.45 | 4.86 | -0.02 | negligible |
| Adverbs | 4.95 | 3.78 | 4.89 | 4.49 | 0.03 | negligible |
| Verbs | 20.88 | 9.04 | 20.97 | 9.76 | -0.02 | negligible |
Part-of-speech (POS) tagging was done using spaCy (Honnibal & Montani, 2017) and the spacyr package (Benoit & Matsuo, 2018). There is one small gender difference (d = \(|0.22|\)) in the use of plural nouns, which make up 5.85% of the words used by female Labour MPs, compared to 5.03% of words spoken by male Labour MPs. As with LIWC, there are no categories where d >= \(|0.2|\) when comparing female Labour MPs by selection process.
The most commonly used words by both men and women would be protocol decorum expressions, so we calculate the keyness of words to identify gender differences in the choices of topics raised by men and women, and by short-list and non-short list women.
Keyness – a linguistic measure of the frequency of different words in two groups of texts – reveals clear gender differences in the most disproportionately common words used by female and male Labour MPs. Unsurprisingly, despite male MPs saying almost twice as many words (30,596,921 vs 15,897,929) as their female colleagues, female Labour MPs were more than two-and-a-half (2.61) times as likely to say “women”. They were also much more likely to use “women’s” and “woman” in parliamentary debate. Female Labour MPs also appear much more likely to discuss “children”, “people”, “care”, “families”, “home”, “parents”, “work” and social policy areas such as “services”, “disabled [people]” and “housing” than their male colleagues. Male MPs were more likely to refer to military topics (“Iraq”, “nuclear”), and to parliamentary process and protocol – “question”, “political”, “conservative”, “electoral”, “house”, “party”, “argument” “liberal” and “point” are far more common in speeches by male Labour MPs than by female ones. This could suggest that male Labour MPs are more comfortable using the traditional language of House of Commons debate, and are more concerned with the rules, procedures and processes of the parliamentary system than their female colleagues.
Keyness between Labour MPs, by Gender
Keyness differences by selection process are not as obviously stereotypical. Nonetheless, the most common words amongst AWS MPs included “carers”, “disabled”, “bedroom” and “sen”2. Also of note is AWS MPs making more references to their “constituency” and its “constituents”, suggesting that AWS MPs may draw on the fact they were elected by their constituents as a source political legitimacy, at least more than non-AWS MPs.
Keyness between Female Labour MPs, by Selection Process
The keyness differences between Labour and Conservative MPs are much greater than gender differences within Labour. The very high use of “Lady” by Conservative MPs is reflective of the greater proportion of female MPs in other parties, as it is often used to refer to comments by other members of the house. It may also represent a greater use of traditional hosue decorum by Conservative MPs.
Keyness between Labour and Conservative MPs
We created bigrams of all first person plural and singular pronouns for female Labour MPs. As above, AWS MPs are far more likely to make references to their constituency or their constituents.
Bigram Keyness in Female Labour MPs by Selection Process
We trained a Naive Bayes classifier with document-frequency priors and a multinomial distribution to predict the gender of speakers when given speeches by all Labour MPs in our dataset, and the selection process when only given female Labour MPs. The accuracy of both models were roughly equivalent, 70.55% accuracy when predicting gender and 70.84% when predicting short lists. By contrast, the classifier could distinguish between Labour and Conservative speeches with 74.23% accuracy.
Using topic models to classify text is widely used in social sciences (Grimmer & Stewart, 2013), as, when combined with the large volume of plain text data available, it allows for a rapid and consistent method of analysis . Topic modelling and other statistic methods of textual analysis are not a substitute for reading the texts themselves, but can augment other analysis or – as in this case – analyse and classify larger amounts of text than would be feasible using human coders (Grimmer & Stewart, 2013). Topic models classify a series of documents (in this case individual speeches) into one of a given number of topics, identifying terms that are common in some documents but rare in others. When developing topic models, there is a trade-off between high precision in the classification of each document with broader topics when using smaller numbers of topics, or lower precision in individual speech classification with more finely-grained topics when using larger numbers of topics. Grimmer & Stewart (2013) also highlight the importance of validating unsurpervised topic models when applied to new sets of texts.
The R package stm (Roberts, Stewart, & Tingley, 2018) implements a structured topic model (STM) (Arora et al., 2013; Roberts, Stewart, & Airoldi, 2016). An STM incorporates data about the writer or speaker into the topic classification algorithm. This differs from traditional topic modelling methods using latent variables to identify topics (e.g. with latent Dirichlet allocation Blei, Ng, & Jordan, 2003), and then comparing proportions of each topic to one or more external variables. STM allows us to incorporate the variables we are interested in to the topic model itself, i.e. the proportion of speechs classified as belonging to each topic can vary as a function of the AWS variable.
We incorporated the AWS status of speakers into our topic model, using all speeches by female Labour MPs, with their AWS status as a covariate in classifying topics. We then matched these topics to speechs by male Labour MPs.
We produced two different structured topic model implementations, with different numbers of topics (K).
The first implementation used an algorithm developed by Lee & Mimno (2014), implemented in the stm package (Roberts et al., 2018), to estimate the number of topics across all speeches made by female Labour MPs, using the “spectral” method developed by Arora et al. (2013), implemented by Roberts et al. (2018). The resulting topic model has 69 topics, across 81,607 documents and a dictionary of 115,477 words. However, the topic quality with K = 69 is poor, and several topics have poor semantic coherence (see , and the appendix).
As seen in the word lists in the appendix, there is relatively scattershot semantic coherence, although exclusivity is high, when using the 69 topic models suggested by Lee and Mimno’s (2014) algorithm. We therefore re-ran the analysis, using 30 topic models, which resulted in increased semantic coherence, albeit with slightly lower exclusivity, as illustrated in Figure . The lower number of models also makes accurate hand-coding of topics possible.
We created a Fruchterman-Reingold (Fruchterman & Reingold, 1991) diagram to show the connections between different topics. Larger vertices indicate more common topics, and the plot uses a colour scale to indicate the proportion of speeches classed in that topic made by AWS and non-AWS female Labour MPs. The space between vertices indicate the closeness of two topics.
Fruchterman-Reingold plot of K30 Network
Coherence of K30 Topic Models
The stm package includes the estimateEffect function to create a regression model using individual documents (speeches) as individual observations, with the number of documents fitting each topic as the dependent variable and model covariates (AWS status) as independent variables.
In all but five topics (gender, immigration, international, children and the environment) the model found p values of < 0.01, and in every topic except the aforemention and “people”, p values of < 0.001.
| Estimate | Standard Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| Topic 1 – Bills | |||||
| Intercept | 0.0449633 | 0.0006498 | 69.1927133 | < 0.001 | *** |
| Short List | -0.0068652 | 0.0008157 | -8.4158760 | < 0.001 | *** |
| Topic 2 – Consultations | |||||
| Intercept | 0.0757456 | 0.0006088 | 124.4135491 | < 0.001 | *** |
| Short List | -0.0214950 | 0.0007211 | -29.8072043 | < 0.001 | *** |
| Topic 3 – Gender | |||||
| Intercept | 0.0230188 | 0.0006054 | 38.0240848 | < 0.001 | *** |
| Short List | -0.0007313 | 0.0006927 | -1.0557346 | 0.29 | |
| Topic 4 – Police | |||||
| Intercept | 0.0289330 | 0.0006350 | 45.5645109 | < 0.001 | *** |
| Short List | -0.0074441 | 0.0007453 | -9.9881995 | < 0.001 | *** |
| Topic 5 – European Union | |||||
| Intercept | 0.0368405 | 0.0006729 | 54.7504749 | < 0.001 | *** |
| Short List | -0.0050989 | 0.0007784 | -6.5507532 | < 0.001 | *** |
| Topic 6 – Transport | |||||
| Intercept | 0.0218012 | 0.0005744 | 37.9563742 | < 0.001 | *** |
| Short List | 0.0054380 | 0.0007675 | 7.0850991 | < 0.001 | *** |
| Topic 7 – Disability | |||||
| Intercept | 0.0298360 | 0.0006461 | 46.1800470 | < 0.001 | *** |
| Short List | 0.0131765 | 0.0007849 | 16.7879674 | < 0.001 | *** |
| Topic 8 – Immigration | |||||
| Intercept | 0.0186039 | 0.0004805 | 38.7189859 | < 0.001 | *** |
| Short List | 0.0004577 | 0.0006259 | 0.7313654 | 0.46 | |
| Topic 9 – Disease | |||||
| Intercept | 0.0249569 | 0.0005888 | 42.3893263 | < 0.001 | *** |
| Short List | -0.0051127 | 0.0007222 | -7.0788362 | < 0.001 | *** |
| Topic 10 – Parties | |||||
| Intercept | 0.0378846 | 0.0004751 | 79.7467610 | < 0.001 | *** |
| Short List | 0.0020066 | 0.0005987 | 3.3514073 | < 0.001 | *** |
| Topic 11 – Health Care | |||||
| Intercept | 0.0407905 | 0.0006961 | 58.5999064 | < 0.001 | *** |
| Short List | -0.0083156 | 0.0008737 | -9.5180094 | < 0.001 | *** |
| Topic 12 – Members | |||||
| Intercept | 0.0389968 | 0.0005993 | 65.0713124 | < 0.001 | *** |
| Short List | 0.0082184 | 0.0007302 | 11.2551023 | < 0.001 | *** |
| Topic 13 – Investment | |||||
| Intercept | 0.0302313 | 0.0006154 | 49.1261500 | < 0.001 | *** |
| Short List | -0.0028620 | 0.0007741 | -3.6972528 | < 0.001 | *** |
| Topic 14 – Crime | |||||
| Intercept | 0.0239303 | 0.0005292 | 45.2214465 | < 0.001 | *** |
| Short List | -0.0032121 | 0.0006371 | -5.0418257 | < 0.001 | *** |
| Topic 15 – Justice | |||||
| Intercept | 0.0409423 | 0.0006878 | 59.5228004 | < 0.001 | *** |
| Short List | -0.0167466 | 0.0008429 | -19.8683165 | < 0.001 | *** |
| Topic 16 – Energy | |||||
| Intercept | 0.0384171 | 0.0006743 | 56.9719478 | < 0.001 | *** |
| Short List | -0.0046100 | 0.0008719 | -5.2874762 | < 0.001 | *** |
| Topic 17 – People | |||||
| Intercept | 0.0788563 | 0.0005752 | 137.0944198 | < 0.001 | *** |
| Short List | 0.0019922 | 0.0007230 | 2.7555854 | 0.006 | ** |
| Topic 18 – Education | |||||
| Intercept | 0.0298510 | 0.0006842 | 43.6284617 | < 0.001 | *** |
| Short List | 0.0038047 | 0.0008443 | 4.5061502 | < 0.001 | *** |
| Topic 19 – Fishing | |||||
| Intercept | 0.0196069 | 0.0004960 | 39.5281208 | < 0.001 | *** |
| Short List | 0.0092594 | 0.0006585 | 14.0621858 | < 0.001 | *** |
| Topic 20 – Housing | |||||
| Intercept | 0.0204434 | 0.0005486 | 37.2667515 | < 0.001 | *** |
| Short List | 0.0038473 | 0.0007514 | 5.1199139 | < 0.001 | *** |
| Topic 21 – Tax & Budgets | |||||
| Intercept | 0.0460797 | 0.0007993 | 57.6466588 | < 0.001 | *** |
| Short List | 0.0142945 | 0.0010081 | 14.1799697 | < 0.001 | *** |
| Topic 22 – Farmers | |||||
| Intercept | 0.0139625 | 0.0004691 | 29.7626596 | < 0.001 | *** |
| Short List | 0.0055240 | 0.0005797 | 9.5290003 | < 0.001 | *** |
| Topic 23 – International | |||||
| Intercept | 0.0261568 | 0.0006786 | 38.5431264 | < 0.001 | *** |
| Short List | 0.0007715 | 0.0008471 | 0.9108196 | 0.36 | |
| Topic 24 – Media | |||||
| Intercept | 0.0124115 | 0.0003960 | 31.3427056 | < 0.001 | *** |
| Short List | 0.0048964 | 0.0004979 | 9.8338502 | < 0.001 | *** |
| Topic 25 – Local authorities | |||||
| Intercept | 0.0426114 | 0.0006104 | 69.8123561 | < 0.001 | *** |
| Short List | -0.0085925 | 0.0007381 | -11.6415862 | < 0.001 | *** |
| Topic 26 – Children | |||||
| Intercept | 0.0250044 | 0.0005685 | 43.9835308 | < 0.001 | *** |
| Short List | -0.0000121 | 0.0007339 | -0.0164345 | 0.99 | |
| Topic 27 – Environment | |||||
| Intercept | 0.0149434 | 0.0004799 | 31.1365480 | < 0.001 | *** |
| Short List | 0.0003248 | 0.0005945 | 0.5463792 | 0.58 | |
| Topic 28 – Ministers | |||||
| Intercept | 0.0427008 | 0.0005833 | 73.2084634 | < 0.001 | *** |
| Short List | 0.0145502 | 0.0007220 | 20.1539326 | < 0.001 | *** |
| Topic 29 – Parliament | |||||
| Intercept | 0.0187969 | 0.0005246 | 35.8334273 | < 0.001 | *** |
| Short List | 0.0063043 | 0.0006868 | 9.1788680 | < 0.001 | *** |
| Topic 30 – Other | |||||
| Intercept | 0.0526683 | 0.0003251 | 161.9821372 | < 0.001 | *** |
| Short List | -0.0037495 | 0.0003943 | -9.5080507 | < 0.001 | *** |
| Topic Number | AWS Speeches | Percent of AWS Speeches | Non-AWS Speeches | Percent of non-AWS Speeches | Male MP Speeches | Percent of Male MP Speeches |
|---|---|---|---|---|---|---|
| Topic 1 | 1,792 | 3.34% | 1,229 | 4.4% | 8,163 | 4.82% |
| Topic 2 | 2,476 | 4.61% | 2,514 | 9.01% | 11,392 | 6.73% |
| Topic 3 | 1,082 | 2.01% | 632 | 2.27% | 926 | 0.55% |
| Topic 4 | 1,303 | 2.43% | 900 | 3.23% | 3,364 | 1.99% |
| Topic 5 | 1,976 | 3.68% | 1,371 | 4.91% | 9,653 | 5.7% |
| Topic 6 | 1,720 | 3.2% | 623 | 2.23% | 4,562 | 2.69% |
| Topic 7 | 2,721 | 5.07% | 758 | 2.72% | 4,045 | 2.39% |
| Topic 8 | 879 | 1.64% | 381 | 1.37% | 2,192 | 1.29% |
| Topic 9 | 1,008 | 1.88% | 743 | 2.66% | 1,747 | 1.03% |
| Topic 10 | 1,351 | 2.52% | 658 | 2.36% | 6,235 | 3.68% |
| Topic 11 | 2,144 | 3.99% | 1,552 | 5.56% | 4,494 | 2.65% |
| Topic 12 | 2,507 | 4.67% | 883 | 3.16% | 10,395 | 6.14% |
| Topic 13 | 1,231 | 2.29% | 825 | 2.96% | 3,972 | 2.35% |
| Topic 14 | 984 | 1.83% | 646 | 2.32% | 1,570 | 0.93% |
| Topic 15 | 1,180 | 2.2% | 1,410 | 5.05% | 4,935 | 2.91% |
| Topic 16 | 2,175 | 4.05% | 1,302 | 4.67% | 7,547 | 4.46% |
| Topic 17 | 5,309 | 9.89% | 2,357 | 8.45% | 25,255 | 14.91% |
| Topic 18 | 2,362 | 4.4% | 1,003 | 3.59% | 6,230 | 3.68% |
| Topic 19 | 1,183 | 2.2% | 445 | 1.59% | 3,305 | 1.95% |
| Topic 20 | 1,334 | 2.48% | 561 | 2.01% | 2,075 | 1.23% |
| Topic 21 | 4,361 | 8.12% | 1,556 | 5.58% | 11,845 | 6.99% |
| Topic 22 | 977 | 1.82% | 359 | 1.29% | 2,259 | 1.33% |
| Topic 23 | 1,787 | 3.33% | 890 | 3.19% | 6,124 | 3.62% |
| Topic 24 | 813 | 1.51% | 233 | 0.84% | 2,132 | 1.26% |
| Topic 25 | 1,604 | 2.99% | 1,104 | 3.96% | 4,917 | 2.9% |
| Topic 26 | 1,237 | 2.3% | 664 | 2.38% | 1,105 | 0.65% |
| Topic 27 | 668 | 1.24% | 325 | 1.16% | 1,796 | 1.06% |
| Topic 28 | 3,218 | 5.99% | 1,001 | 3.59% | 8,906 | 5.26% |
| Topic 29 | 1,121 | 2.09% | 304 | 1.09% | 4,463 | 2.64% |
| Topic 30 | 1,202 | 2.24% | 673 | 2.41% | 3,746 | 2.21% |
K30 Pyramid Chart
K30 Bar Chart
AWS are – proportionally – more likely than non-AWS MPs to discuss Topics 29 (parliament), 7 (disability) and 24 (media). They are proportionally less likely to mention Topics 15 (justice), 2 (consultations) and 9 (disease). See Figure for more details. Perhaps surprisingly, AWS MPs are slightly less likely to mention gender issues (Topic 3), although the difference is not statistically significant (see the appendix for details).
K30 Topic Proportions
| Topic Number | Top Ten Words | Top Ten FREX |
|---|---|---|
| Topic 1 | bill, amendment, clause, new, legislation, amendments, act, committee, provisions, 1 | amendment, clause, amendments, clauses, nos, insert, subsection, provisions, bill, tabled |
| Topic 2 | issues, public, information, also, report, review, process, work, need, important | consultation, review, guidance, recommendations, information, considering, decisions, arrangements, framework, detailed |
| Topic 3 | women, men, pay, equality, rights, women’s, discrimination, equal, work, woman | women, equality, gender, equalities, bishops, discrimination, female, women’s, equal, men |
| Topic 4 | police, crime, officers, behaviour, policing, home, antisocial, community, work, force | policing, antisocial, constable, burglary, wardens, crime, constabulary, police, officers, pcsos |
| Topic 5 | european, uk, countries, eu, union, trade, international, united, world, british | treaty, enlargement, wto, lisbon, doha, eu, eu’s, mod, multilateral, accession |
| Topic 6 | transport, london, rail, bus, road, services, line, travel, network, train | rail, bus, passengers, fares, trains, buses, passenger, heathrow, congestion, hs2 |
| Topic 7 | people, work, benefit, pension, benefits, support, disabled, employment, carers, working | disabled, jobcentre, incapacity, carers, pension, claimants, esa, dla, pensions, atos |
| Topic 8 | immigration, safety, uk, asylum, enforcement, home, number, illegal, licensing, animals | dogs, dog, id, visa, fur, mink, hse, sia, seekers, fireworks |
| Topic 9 | health, research, cancer, treatment, medical, disease, can, smoking, patients, people | cancer, diseases, vaccine, flu, embryos, infections, diabetes, palliative, prostate, cervical |
| Topic 10 | government, labour, conservative, party, opposition, policy, government’s, scotland, scottish, members | conservative, liberal, democrats, conservatives, scottish, democrat, scotland, tory, interruption, tories |
| Topic 11 | care, health, nhs, services, service, hospital, patients, staff, trust, social | dentists, ambulance, dentistry, helier, dentist, nurses, hospital, pct, hospitals, dental |
| Topic 12 | member, members, debate, house, mr, committee, said, time, speaker, north | member, speaker, mr, debate, spoke, thoughtful, backbench, debates, madam, select |
| Topic 13 | companies, financial, company, market, scheme, money, debt, consumers, bank, credit | payday, annuity, oft, policyholders, penrose, fca, loan, prepayment, loans, annuities |
| Topic 14 | young, people, health, mental, youth, prison, problems, drugs, alcohol, drug | prisons, probation, cannabis, reoffending, mental, prison, self-harm, youth, alcohol, sentences |
| Topic 15 | cases, court, legal, law, case, justice, evidence, criminal, courts, home | judicial, attorney-general, defendant, extradition, tpims, suspects, court, courts, prosecution, isc |
| Topic 16 | energy, businesses, business, jobs, investment, economy, industry, economic, new, sector | carbon, renewable, renewables, solar, low-carbon, energy, feed-in, manufacturing, steel, businesses |
| Topic 17 | people, want, one, get, know, say, us, many, think, need | things, think, something, get, want, going, really, say, lot, go |
| Topic 18 | education, schools, school, children, training, skills, parents, teachers, students, young | schools, teachers, pupils, curriculum, sen, academies, ofsted, pupil, grammar, attainment |
| Topic 19 | constituency, city, people, many, years, work, centre, one, hull, great | fishermen, cod, hull, plymouth, maiden, fishing, fish, humber, fleetwood, tourism |
| Topic 20 | housing, homes, people, private, london, social, home, affordable, need, accommodation | rent, tenants, landlords, rented, homelessness, homeless, rents, tenancies, housing, tenancy |
| Topic 21 | tax, year, million, government, budget, cuts, cut, poverty, increase, billion | tax, obr, vat, millionaires, 50p, inflation, budget, fiscal, chancellor, cut |
| Topic 22 | food, post, office, rural, petition, offices, farmers, royal, mail, government | petition, farmers, petitioners, meat, cull, labelling, cattle, badger, culling, beef |
| Topic 23 | people, international, human, government, war, rights, country, un, conflict, world | syria, israel, civilians, palestinian, israeli, gaza, sri, holocaust, hatred, sierra |
| Topic 24 | bbc, media, online, internet, sport, access, digital, culture, clubs, football | bbc, games, olympic, gambling, bbc’s, copyright, lap-dancing, broadband, radio, internet |
| Topic 25 | local, authorities, funding, areas, services, council, community, authority, government, communities | local, authorities, funding, councils, grant, authority, formula, deprived, areas, partnership |
| Topic 26 | children, child, families, care, family, parents, violence, support, domestic, victims | trafficked, csa, same-sex, adopters, child, rape, marriages, marriage, sexual, couples |
| Topic 27 | planning, water, development, land, environment, site, sites, flood, environmental, area | forestry, biodiversity, masts, habitats, gypsy, flood, waterways, flooding, marine, mmo |
| Topic 28 | secretary, state, house, last, statement, report, said, now, question, answer | secretary, statement, state, confirm, official, answer, vol, state’s, letter, written |
| Topic 29 | parliament, wales, vote, commission, political, assembly, people, welsh, elected, charities | electoral, polling, gibraltar, voting, assembly, vote, votes, voter, ballot, elections |
| Topic 30 | can, make, ensure, agree, important, take, made, point, sure, welcome | agree, aware, sure, ensure, taking, lady, welcome, steps, point, make |
There do not appear to be substantial or meaningful differences in the speaking styles of female Labour MPs selected through all women short lists when compared to their female colleagues selected through open short lists using LIWC. This is possibly due to the speaking style dominant in British parliamentary debate, which is more formal than the speech used in most day-to-day conversation. LIWC has American developers, and the dictionary may not be able to capture stylistic differences between American and British English, and may not include words commonly used in formal British English speech, limiting its usefulness in a British context.
There is more gender distinction in some selected terms and topics. AWS MPs are far more likely to make reference to their constituency and their constituents. In the debate between whether MPs should be “delegates” or “trustees” – the “mandate-independence controversy” outlined by Pitkin (1967) – the references to their constituents and constituencies suggests AWS MPs shy away from the Burkean concept of trusteeship and see themselves more as strict representatives of their constituents. In Andeweg & Thomassen’s (2005) typology of ex ante/ex post and above/below political representation, AWS MPs lean towards representation “from below”, although their selection process is ex ante/ex post.
AWS MPs refer to their constituents both specifically and in the abstract, particularly when criticising government policy. For example, in debate on 4th March 2015, Gemma Doyle, than the Labour MP for West Dunbartonshire (elected on an AWS in 2010), when asked if she would give way to Conservative MP Stephen Mosley, responded:
No, I will not [give way], because my constituents want me to make these points, not to give more time to Conservative Members.
On 2nd June 2010, during debate on Israel-Palestine, Valerie Vaz, MP for Walsall South:
My constituents want more than pressure. Will the Foreign Secretary come back to the House and report on a timetable for the discussions on a diplomatic solution, just as we did on Ireland?
On 4th April 2001, Betty Williams, member for Conwy from 1997–2010, raised the case of a wilderness guide in her constituency unable to access parts of the countryside due to foot and mouth disease:
Is my right hon. Friend aware that there is continuing concern about the limited access to the countryside and crags of north Wales? May I draw his attention to the circumstances of my constituent, Ric Potter? Like many others, he has had to travel to Scotland, where there is greater access. Will my right hon. Friend help us to enable people such as Ric Potter to find work in outdoor pursuits?
## A topic model with 30 topics, 81607 documents and a 115477 word dictionary.
## Topic 1 Top Words:
## Highest Prob: bill, amendment, clause, new, legislation, amendments, act
## FREX: amendment, clause, amendments, clauses, nos, insert, subsection
## Lift: #185, #85, 0.003, 05, 1-competences, 1-impact, 1,924
## Score: clause, amendment, amendments, bill, provisions, lords, nos
## Topic 2 Top Words:
## Highest Prob: issues, public, information, also, report, review, process
## FREX: consultation, review, guidance, recommendations, information, considering, decisions
## Lift: 1-who, 1,842, 109648, 1402, 151387, 1981-was, 1a-has
## Score: consultation, guidance, information, review, committee, issues, process
## Topic 3 Top Words:
## Highest Prob: women, men, pay, equality, rights, women's, discrimination
## FREX: women, equality, gender, equalities, bishops, discrimination, female
## Lift: gender, #112, #neverthelesshepersisted, 1-breast-feed, 1,087, 1,574, 1.57
## Score: women, women's, equality, men, gender, discrimination, girls
## Topic 4 Top Words:
## Highest Prob: police, crime, officers, behaviour, policing, home, antisocial
## FREX: policing, antisocial, constable, burglary, wardens, crime, constabulary
## Lift: 1,113, 1.24, 17,614, acpo's, adz, alcohol-free, alleygator
## Score: police, crime, officers, policing, antisocial, behaviour, constable
## Topic 5 Top Words:
## Highest Prob: european, uk, countries, eu, union, trade, international
## FREX: treaty, enlargement, wto, lisbon, doha, eu, eu's
## Lift: #420, 0.26, 0.56, 07, 09, 1-2, 1-of
## Score: eu, european, countries, treaty, armed, defence, forces
## Topic 6 Top Words:
## Highest Prob: transport, london, rail, bus, road, services, line
## FREX: rail, bus, passengers, fares, trains, buses, passenger
## Lift: #145, 0.1p, 0.45, 0.86, 1-very, 1,122, 1,658
## Score: rail, transport, bus, passengers, fares, trains, congestion
## Topic 7 Top Words:
## Highest Prob: people, work, benefit, pension, benefits, support, disabled
## FREX: disabled, jobcentre, incapacity, carers, pension, claimants, esa
## Lift: dla, #400, 0300, 1-to-1, 1,030, 1,052, 1,366
## Score: pension, carers, disabled, pensions, allowance, disability, credit
## Topic 8 Top Words:
## Highest Prob: immigration, safety, uk, asylum, enforcement, home, number
## FREX: dogs, dog, id, visa, fur, mink, hse
## Lift: 44a, a8, acoba, arcs, attachment-free, bareboat, bonfires
## Score: immigration, asylum, animals, dogs, fireworks, dog, animal
## Topic 9 Top Words:
## Highest Prob: health, research, cancer, treatment, medical, disease, can
## FREX: cancer, diseases, vaccine, flu, embryos, infections, diabetes
## Lift: 1169, 20-fold, ablation, abnormalities, adpkd, aed, anaesthesia
## Score: cancer, patients, disease, smoking, health, diagnosis, screening
## Topic 10 Top Words:
## Highest Prob: government, labour, conservative, party, opposition, policy, government's
## FREX: conservative, liberal, democrats, conservatives, scottish, democrat, scotland
## Lift: #nationalistsconfused, 1-but, 1.135, 10,182, 10.91, 1125, 116385
## Score: conservative, scottish, party, labour, government, scotland, liberal
## Topic 11 Top Words:
## Highest Prob: care, health, nhs, services, service, hospital, patients
## FREX: dentists, ambulance, dentistry, helier, dentist, nurses, hospital
## Lift: 2.24, 2005-6, 22,600, 422, 5.45pm, 8.03, 8.41
## Score: nhs, patients, care, hospital, health, patient, hospitals
## Topic 12 Top Words:
## Highest Prob: member, members, debate, house, mr, committee, said
## FREX: member, speaker, mr, debate, spoke, thoughtful, backbench
## Lift: e-petitions, @daisydumble, @percyblakeney63, 10,000-signature, 1028, 1080, 11.00
## Score: member, mr, committee, members, speaker, debate, house
## Topic 13 Top Words:
## Highest Prob: companies, financial, company, market, scheme, money, debt
## FREX: payday, annuity, oft, policyholders, penrose, fca, loan
## Lift: fca, oft, prepayment, #1.8, #20,000, 0.21, 0.84
## Score: companies, consumers, fsa, banks, company, customers, consumer
## Topic 14 Top Words:
## Highest Prob: young, people, health, mental, youth, prison, problems
## FREX: prisons, probation, cannabis, reoffending, mental, prison, self-harm
## Lift: cannabis, hawton, poppers, camhs, inmates, reoffending, #230
## Score: young, mental, prison, drugs, alcohol, youth, drug
## Topic 15 Top Words:
## Highest Prob: cases, court, legal, law, case, justice, evidence
## FREX: judicial, attorney-general, defendant, extradition, tpims, suspects, court
## Lift: 110-day, abscond, absconded, acquittals, adduce, anti-viral, babar
## Score: court, offence, courts, criminal, justice, prosecution, offences
## Topic 16 Top Words:
## Highest Prob: energy, businesses, business, jobs, investment, economy, industry
## FREX: carbon, renewable, renewables, solar, low-carbon, energy, feed-in
## Lift: fossil, sellafield, viyella, energy-intensive, low-carbon, #12.5, #140,000
## Score: energy, businesses, jobs, economy, manufacturing, industry, investment
## Topic 17 Top Words:
## Highest Prob: people, want, one, get, know, say, us
## FREX: things, think, something, get, want, going, really
## Lift: 1,027, 2.85, 30s-will, 6.37, 778, about-part, accept-there
## Score: people, get, think, things, going, want, say
## Topic 18 Top Words:
## Highest Prob: education, schools, school, children, training, skills, parents
## FREX: schools, teachers, pupils, curriculum, sen, academies, ofsted
## Lift: ema, #8,000, 1,000-pupil, 1,051, 1,100-i, 1,170, 1,204
## Score: schools, school, education, children, teachers, pupils, students
## Topic 19 Top Words:
## Highest Prob: constituency, city, people, many, years, work, centre
## FREX: fishermen, cod, hull, plymouth, maiden, fishing, fish
## Lift: #14.4, #66.6, 0.27, 0.51, 1,084, 1,126, 1.41
## Score: plymouth, constituency, hull, city, fishing, fish, arts
## Topic 20 Top Words:
## Highest Prob: housing, homes, people, private, london, social, home
## FREX: rent, tenants, landlords, rented, homelessness, homeless, rents
## Lift: right-to-buy, #19, #21.5, #28.5, 1,000-odd, 1,026, 1,083
## Score: housing, homes, rented, rent, tenants, landlords, affordable
## Topic 21 Top Words:
## Highest Prob: tax, year, million, government, budget, cuts, cut
## FREX: tax, obr, vat, millionaires, 50p, inflation, budget
## Lift: 0.38, 1,869, 107,500, 11.2, 13,600, 2,073, 2.33
## Score: tax, cuts, budget, poverty, chancellor, unemployment, billion
## Topic 22 Top Words:
## Highest Prob: food, post, office, rural, petition, offices, farmers
## FREX: petition, farmers, petitioners, meat, cull, labelling, cattle
## Lift: #450, 1072, 11,900, 12-point, 934, a690, ablewell
## Score: food, farmers, petitioners, petition, post, rural, offices
## Topic 23 Top Words:
## Highest Prob: people, international, human, government, war, rights, country
## FREX: syria, israel, civilians, palestinian, israeli, gaza, sri
## Lift: muslims, #aleppo, #no2lgbthate, 0.002, 1,000-almost, 1,010, 1,019
## Score: syria, un, israel, humanitarian, iraq, palestinian, israeli
## Topic 24 Top Words:
## Highest Prob: bbc, media, online, internet, sport, access, digital
## FREX: bbc, games, olympic, gambling, bbc's, copyright, lap-dancing
## Lift: age-restricted, age-verification, aquatics, bacta, bandwidth, bbfc, bduk
## Score: bbc, sport, tickets, internet, digital, online, football
## Topic 25 Top Words:
## Highest Prob: local, authorities, funding, areas, services, council, community
## FREX: local, authorities, funding, councils, grant, authority, formula
## Lift: 416,000, 596,000, 82-3, 885, allison's, baccy, bellwin
## Score: local, authorities, funding, councils, authority, council, services
## Topic 26 Top Words:
## Highest Prob: children, child, families, care, family, parents, violence
## FREX: trafficked, csa, same-sex, adopters, child, rape, marriages
## Lift: @mandatenow, 1-regardless, 1,000-discriminates, 1,142,600, 1,483, 1,746, 10-month-old
## Score: child, children, parents, violence, care, sexual, rape
## Topic 27 Top Words:
## Highest Prob: planning, water, development, land, environment, site, sites
## FREX: forestry, biodiversity, masts, habitats, gypsy, flood, waterways
## Lift: biodiversity, encampments, masts, #tartantories, 0fficial, 1,000-year-old, 1,251
## Score: planning, land, flood, marine, sites, water, site
## Topic 28 Top Words:
## Highest Prob: secretary, state, house, last, statement, report, said
## FREX: secretary, statement, state, confirm, official, answer, vol
## Lift: 12.40, ashleys, ayia, burne, cabinet's, cairns's, clutha
## Score: secretary, state, statement, answer, confirm, inquiry, leader
## Topic 29 Top Words:
## Highest Prob: parliament, wales, vote, commission, political, assembly, people
## FREX: electoral, polling, gibraltar, voting, assembly, vote, votes
## Lift: @leamingtonsbc, @maggieannehayes, @nhconsortium, #keeptweeting, 1-46, 1-would, 1,294
## Score: electoral, vote, elections, wales, assembly, referendum, welsh
## Topic 30 Top Words:
## Highest Prob: can, make, ensure, agree, important, take, made
## FREX: agree, aware, sure, ensure, taking, lady, welcome
## Lift: 1565, 19602, 2,095, 42931, 94254, agencies-an, anguish-filled
## Score: agree, aware, thank, ensure, point, lady, can
##
## Call:
## estimateEffect(formula = 1:30 ~ short_list, stmobj = topic_model_k30,
## metadata = lab_corpus_fem_stm$meta, uncertainty = "Global")
##
##
## Topic 1:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0449755 0.0006472 69.493 <0.0000000000000002 ***
## short_listTRUE -0.0068779 0.0008151 -8.439 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 2:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0757511 0.0006095 124.28 <0.0000000000000002 ***
## short_listTRUE -0.0215037 0.0007218 -29.79 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 3:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0230304 0.0006035 38.161 <0.0000000000000002 ***
## short_listTRUE -0.0007445 0.0006951 -1.071 0.284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 4:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0289390 0.0006449 44.870 <0.0000000000000002 ***
## short_listTRUE -0.0074465 0.0007507 -9.919 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 5:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0368433 0.0006683 55.128 < 0.0000000000000002 ***
## short_listTRUE -0.0050970 0.0007776 -6.555 0.0000000000559 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 6:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0217966 0.0005682 38.36 < 0.0000000000000002 ***
## short_listTRUE 0.0054412 0.0007568 7.19 0.000000000000653 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 7:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0298430 0.0006534 45.68 <0.0000000000000002 ***
## short_listTRUE 0.0131664 0.0007960 16.54 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 8:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0186040 0.0004866 38.233 <0.0000000000000002 ***
## short_listTRUE 0.0004542 0.0006331 0.717 0.473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 9:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0249398 0.0005900 42.274 < 0.0000000000000002 ***
## short_listTRUE -0.0050920 0.0007184 -7.087 0.00000000000138 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 10:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0378826 0.0004769 79.442 < 0.0000000000000002 ***
## short_listTRUE 0.0020076 0.0005988 3.353 0.000801 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 11:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0407964 0.0006946 58.736 <0.0000000000000002 ***
## short_listTRUE -0.0083296 0.0008782 -9.485 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 12:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0389920 0.0005983 65.17 <0.0000000000000002 ***
## short_listTRUE 0.0082206 0.0007271 11.31 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 13:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0302234 0.0006155 49.102 < 0.0000000000000002 ***
## short_listTRUE -0.0028531 0.0007772 -3.671 0.000241 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 14:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0239267 0.0005272 45.38 < 0.0000000000000002 ***
## short_listTRUE -0.0032073 0.0006326 -5.07 0.000000398 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 15:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0409498 0.0006912 59.24 <0.0000000000000002 ***
## short_listTRUE -0.0167429 0.0008410 -19.91 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 16:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0384203 0.0006693 57.403 < 0.0000000000000002 ***
## short_listTRUE -0.0046108 0.0008739 -5.276 0.000000132 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 17:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0788541 0.0005734 137.511 < 0.0000000000000002 ***
## short_listTRUE 0.0019922 0.0007215 2.761 0.00576 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 18:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0298378 0.0006936 43.018 < 0.0000000000000002 ***
## short_listTRUE 0.0038174 0.0008514 4.483 0.00000735 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 19:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0196109 0.0005058 38.77 <0.0000000000000002 ***
## short_listTRUE 0.0092466 0.0006647 13.91 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 20:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0204377 0.0005469 37.372 < 0.0000000000000002 ***
## short_listTRUE 0.0038546 0.0007434 5.185 0.000000216 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 21:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0460818 0.0008017 57.48 <0.0000000000000002 ***
## short_listTRUE 0.0143004 0.0010069 14.20 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 22:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0139579 0.0004672 29.879 <0.0000000000000002 ***
## short_listTRUE 0.0055290 0.0005755 9.608 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 23:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0261672 0.0006795 38.507 <0.0000000000000002 ***
## short_listTRUE 0.0007523 0.0008413 0.894 0.371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 24:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0124090 0.0003942 31.477 <0.0000000000000002 ***
## short_listTRUE 0.0049032 0.0004912 9.983 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 25:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0426122 0.0006095 69.91 <0.0000000000000002 ***
## short_listTRUE -0.0085951 0.0007406 -11.61 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 26:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02500845 0.00056664 44.134 <0.0000000000000002 ***
## short_listTRUE -0.00001695 0.00073195 -0.023 0.982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 27:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0149391 0.0004787 31.208 <0.0000000000000002 ***
## short_listTRUE 0.0003306 0.0005963 0.554 0.579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 28:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0427050 0.0005816 73.42 <0.0000000000000002 ***
## short_listTRUE 0.0145487 0.0007200 20.21 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 29:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0187999 0.0005248 35.823 <0.0000000000000002 ***
## short_listTRUE 0.0063006 0.0006838 9.214 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 30:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0526695 0.0003205 164.348 <0.0000000000000002 ***
## short_listTRUE -0.0037490 0.0003892 -9.634 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
A random selection of 2% of all references to “my constituency”, “my constituent” and “my constituents”, by AWS MPs, in context.
| Pre | Keyword | Post |
|---|---|---|
| . Let me take this opportunity to welcome initiatives in | my constituency | , particularly the Skypad unit , which is based at |
| pill . Can my right hon . Friend say where | my constituents | can turn now for the justice they ought to receive |
| that there has been an incredible growth , certainly in | my constituency | , in the number of people having to resort to |
| During the two years I have been meeting farmers in | my constituency | and making representations to Ministers , many of the issues |
| My constituent | Enola Halleron-Clarke , who is 11 years old , suffers | |
| The George Thomas hospice in | my constituency-a | charity named after one of your predecessors , Mr . |
| broad thrust of the Budget is very bad news for | my constituents | . Hull North will see more individuals out of work |
| Ratcliffe and Gretton . Yes , as the name of | my constituency | suggests , the largest town is that of Burton upon |
| but it is of no interest whatsoever to most of | my constituents | , who do not have the time to intellectualise about |
| like some clarification of how it will affect people in | my constituent’s | position . n " |
| has caused widespread and persistent congestion on the roads in | my constituency | . However , the story of Bradford is not unique |
| it has at St Helier hospital which serves people in | my constituency | . It has , however , been full of praise |
| Churches in | my constituency | have a link with Lesotho that goes back many years |
| n " , " I want to set out what | my constituents | would like to happen . First , I will address |
| effort has failed . This is such an occasion . | My constituent | , Mr . Aranda , first saw me at one |
| . and hon . Members , I have been encouraging | my constituents | to take advantage of the scheme . Will the Minister |
| to conclude my remarks . n " , " | My constituents | ask me these questions . What happens if Lewisham is |
| housing ? Will he applaud the initiatives already taken in | my constituency | , and especially in the Vauxhall area , where health |
| welcome that £ 200 . What advice can he give | my constituents | when faced with high council tax hikes from the Liberal |
| become more competitive . I have many small businesses in | my constituency | . Since I was elected , they have repeatedly raised |
| investment in innovators in microgeneration , such as those in | my constituency | , so that people can make genuine choices about fuel |
| Jobcentre Plus . The Scottish National party Government have excluded | my constituency | from any assistance such as from enterprise zones and the |
| Have the Government any plans to tackle this and help | my constituents | get on the housing ladder ? |
| constituency . I have urban areas to the north-east of | my constituency | and a very large rural area running right up to |
| Allison’s chemist in Cockermouth , which is in | my constituency | , provided a very important resource for local people after |
| supply ? Residents in Sway road , Morriston , in | my constituency | have been threatened with such action because of a fault |
| been contacted by nearly 50 local police officers living in | my constituency | . Not only are they fearful for their jobs but |
| described as the end of local democracy . Many of | my constituents | might argue that that has already happened , given that |
| councils , so will the Secretary of State explain to | my constituents | why county councils are getting additional moneys , but not |
| , 29 April , and 23 May , but for | my constituents | this may not happen until the process is further down |
| constituency are viable businesses that benefit the community , and | my constituents | want them to remain so . Will he confirm that |
| I am really concerned about one of the schools in | my constituency-I | have mentioned it to Ministers before-where only 11 per cent |
| and secured against their own homes were being quoted to | my constituents | . Was this a condition the council set for the |
| . Notwithstanding the fact that the track that would benefit | my constituents | lies in her constituency , her constituents would benefit from |
| , the 37 AONBs , including the Wye valley in | my constituency | , and the Council for the Protection of Rural England |
| in my constituency . It is the biggest employer in | my constituency | , so it is extremely important to me and my |
| grateful for this opportunity to pass on the thanks of | my constituents | to the Government for being serious about tackling health inequalities |
| success since I made my maiden speech on employment in | my constituency | during the debate on the first Queen’s Speech under the |
| a credit to Westminster . n " , " | My constituency | gave the world household names such as Pilkington and Beechams |
| much power just in transmission . As a gentleman in | my constituency | says , it is always either windy , sunny or |
| paying . They have not met the 4,100 people in | my constituency | who cannot find work-more than a quarter of them are |
| a new building for the Methodist community in Boothstown in | my constituency | . It will be a genuinely multi-use building . In |
| Cumbria and near-misses in many other places , including in | my constituency | . We know that the emergency services are providing excellent |
| coverage of rugby league , which is hugely popular in | my constituency | . |
| this change in this Bill to ensure that electors in | my constituency | never have to have this terrible experience again . |
| banks , such as the Brick in the centre of | my constituency | , are lengthening by the day . In the past |
| , what guarantees can the Secretary of State give to | my constituents | that they will be fully informed of the risks associated |
| energy companies and changes to the energy company obligation , | my constituent | may no longer get his hard-to-heat , solid-wall home insulated |
| n " , " No sense of understanding of | my constituent’s | position was given in that final paragraph . The CSA |
| , it looked at Barton Hill , an estate in | my constituency | that is in a ward ranked 133rd on the national |
| Trafford , which has a Conservative council and is where | my constituency | is located . We are seeing a twin squeeze , |
| world . It is a particular issue for many of | my constituents | , and the violence and human rights abuses have spanned |
| bandied around-because I want to talk about the reality that | my constituency | faces . Our health and social provision has developed into |
| of life and death . n " , " | My constituent | Royston Brett set off on Friday and has cycled almost |
| to some people than to others . For people in | my constituency | , the Government’s changes so far have resulted in a |
| Bill on social care , if the Minister thinks that | my constituents | are happy with the package we have before us at |
| Alstom Power , a company that makes gas turbines in | my constituency | , has taken up the challenge . At its conference |
| Chamber , I have been contacted by a number of | my constituents | who are concerned about the Bill and would rather see |
| again have a university . However , Nene college in | my constituency | hopes to change all that , and I support strongly |
| That is the right way forward . Each person in | my constituency | gets £ 7 less than in the neighbouring constituency of |
| , but those have not materialised , leaving women in | my constituency | and many others across the UK facing hardship , stress |
| in this debate , which is of great importance to | my constituency | . I wish to draw in particular from the circumstances |
| I have been making complaints on behalf of | my constituents | for some time about the poor performance of rail services |
| interest that I wish to raise . A number of | my constituents | are families bereaved as a result of the Hillsborough disaster |
| , which have already proved important in creating jobs in | my constituency | . Is he aware that many employers , including those |
| I have described , and frustration on the part of | my constituent | , we received the letter dated 18 September that I |
| women . If the services were moved , some of | my constituents | simply could not access them as they should . |
| health problems ? Will he look into the case of | my constituent | , Mrs . Jenkins , whose disability living allowance was |
| hope I will soon be in a position to reassure | my constituents | that much-needed investment in Lewisham homes will be forthcoming . |
| to say that we have some of that excellence in | my constituency | , with companies such as Baxi Potterton , Aircelle , |
| . Mullin ) . This is a big issue in | my constituency | , where inappropriate development on garden sites is taking place |
| west coast services that link Bangor and Llandudno , in | my constituency | , to London . As a regular user , and |
| and Sir Malcolm Thornton . All have represented part of | my constituency | and all left this House on 20 April or 1 |
| When I met | my constituents | in Highway ward on Saturday , they told me that |
| can make a clear distinction between the two . In | my constituency | , one thing of which I am most proud is |
| matter of great importance to the people of Merseyside and | my constituents | , even though it relates to the South Yorkshire police |
| prison . An ombudsman’s inquiry takes time and , as | my constituent | says , time is not on his side . In |
| they spend their money . n " , " | My constituency | has a close relationship with the textiles industry . Recently |
| The outcome of the consultations , e-consultations and debates around | my constituency | was finely balanced . A small majority felt that there |
| institutions , customers and members , but just as all | my constituents | stand to gain from the contribution that the Bill can |
| not sure whether that is correct because several hundred of | my constituents | write to me regularly . For example , Carrie Flint |
| Over the weekend , 400 job losses were announced in | my constituency | , with the closure of the Stampworks in Ayr , |
| many communities because 12 of the 48 post offices in | my constituency | are proposed for closure . The Government recognise the social |
| which was only yesterday . I sent a response from | my constituency | . I can hope only that it had some influence |
| 39 of the Crown post offices , including Lancaster in | my constituency-and | the relationship MPs have with Post Office Ltd ? Many |
| ’ Bills were introduced on this issue , one by | my constituency | neighbour , my hon . Friend the Member for Brigg |
| expensive failure ? It has been a cost-effective success in | my constituency | . |
| already taken several interventions . n " , " | My constituent | was in work and owned his own home , and |
| services , for the time that he took to greet | my constituents | and for the care and the honesty with which he |
| remain extremely important employers in Burton upon Trent , in | my constituency | . There has been a gradual decline in the number |
| legacy of BSE and , in the northern area of | my constituency | , cattle testing positive for TB , which is a |
| of the problems , and one issue that some of | my constituents | have raised is that written reports from their doctors or |
| by the necessary funding arrangements , so that families in | my constituency | will see the materialisation of extra child care places that |
| In | my constituency | we obviously welcome the two new aircraft carriers and the |
| everything will be fine . n " , " | My constituent | Joe-one of the many people I talked to in the |
| pooh-poohing of the Bevan Foundation’s inquiry and report , but | my constituency | took part in that inquiry and I did not see |
| how important the review of children’s heart surgery is for | my constituents | , as it is for those of each of the |
| she work with other Departments , so that pensioners in | my constituency | and elsewhere can have good , warm housing , a |
| certainly not going to provide for as many people in | my constituency | as EMA did . Although Conservative Members can talk about |
| is perhaps because I have a truly magnificent cathedral in | my constituency | that is over 1,000 years old that I feel strongly |
| , but it is an issue of real importance to | my constituency | . n " , " Cockenzie’s coal-fired power station |
| tax will cost our local economy £ 1.9 million in | my constituency | and almost twice that in Salford , because tenants will |
| to deliver solar panels on public and community buildings in | my constituency | . It told me , in relation to the cut |
| " Many of | my constituents | who work in the probation service have written to me |
| she aware of very similar problems at Samworth Brothers in | my constituency | ? Long-serving workers are seeing their night shift and weekend |
| n " , " Many small business people in | my constituency | are struggling to stay afloat , particularly in the face |
| , but I can tell the hon . Gentleman that | my constituents | do not see it as fair . n " |
| he aware that although PEP is available to some of | my constituents | in Hove and Portslade , it is unfortunately not available |
| a year . I know that many such people in | my constituency | will not get any benefit afterwards because they will probably |
| only language I speak fluently is English . But in | my constituency | in the city of Bristol , 91 different languages are |
| local licensing schemes , because in my experience , in | my constituency | , people want more regulation , not less ? |
| sympathise with the anxieties of people in Ruislip-Northwood , but | my constituents | in north Paddington very warmly welcome the decision on the |
| Adjournment debate , I would like to pay tribute to | my constituent | Nicola Braniff , the partner of the late Stephen O’Malley |
| throughout the constituency . I will strive to serve all | my constituents | to the best of my ability . n " |
| agree with the sentiment . n " , " | My constituent | continues : n " , " " I could |
| to present this petition on UK aid on behalf of | my constituents | . The signatures were n " , " gathered |
| that organisations such as Rucksack and other small charities in | my constituency | , such as Harbour Place , will say that they |
| , much to my disappointment and the huge disappointment of | my constituency | and borough , which had the second-largest vote in the |
| next to impossible , as there is no way for | my constituents | to contact Concentrix . The HMRC has a hotline for |
| for that helpful reply . Does she appreciate that in | my constituency | , during a period of about a year , two |
| we need for our industry . We are lucky in | my constituency | to have rail and the M4 , which means that |
| I believe that | my constituency | contains more BNP councillors than any other constituency in the |
| I was-at Calderhead school in Shotts , which is still | my constituency | . I clearly recall that , when I asked those |
| havoc they wreak on estates in my constituency , and | my constituency | is not an exception . If we are serious about |
| consumer protection Bill , which will provide important safeguards for | my constituents | . People in Northampton carry high levels of credit . |
| In | my constituency | , long-term unemployment has increased by almost 600 % in |
| Many of | my constituents | will have been deeply concerned by the admission of Peter |
| up and take part in this debate and to answer | my constituents | ’ questions . I have something of interest to tell |
| to young girls , many of whom are born in | my constituency | . It is totally unacceptable that the human rights of |
| to happen . n " , " People in | my constituency | have looked at the range of care that needs to |
| the specific case-which I can refer to him-of one of | my constituents | who was bankrupted after a VAT inquiry ? |
| I want to share with the House one anecdote from | my constituency | . When I was campaigning and canvassing in the general |
| time to debate miscarriages of justice ? Last night , | my constituent | , Mr . Michael O’Brien , accepted £ 300,000 in |
| , as others have pointed out this morning . In | my constituency | , there is a significant problem with assaults in the |
| , Sue Essex , has been a huge success . | My constituents | have told me that it has enabled many of them |
| make such a major difference at the north end of | my constituency | . |
| Does he agree , however , that the lives of | my constituents | and many others are blighted by these trees , and |
| As well as big universities such as the ones in | my constituency | , I am concerned about smaller universities , which often |
| that was going into the eco-village in Weardale , in | my constituency | ? That would have created many green jobs in an |
| insurance industry . What is she going to say to | my constituents | ? |
| the affected areas of need . The Weaver ward in | my constituency | is part of the Staffordshire-Derbyshire objective 5b area , demonstrating |
| On 6 December , | my constituent | , Kabba Kamara , was tragically stabbed to death while |
| . n " , " Like many others , | my constituency | is one of great contrasts , from the leafy streets |
| . We are on the west coast main line . | My constituency | contains four rail stations , three of which are on |
| Mary Stevens hospice in Stourbridge is much loved by all | my constituents-so | much so that it derives 82 per cent . of |
| to the most vulnerable consumers . Many of those in | my constituency | are forced to use expensive prepayment cards ; what is |
| the Minister for Pensions Reform for meeting me to discuss | my constituents | ’ case . I hope that amendments to the Bill |
| n " , " Let me highlight three examples from | my constituency | caseload that illustrate the vulnerability of many people who have |
| constituents . It is probably the most important issue in | my constituency | , and has been for a considerable time . It |
| clients , people seeking debt advice in East Lothian , | my constituency | , are now saddled with average payday loan debts of |
| who was concerned about the level of CCTV coverage in | my constituency | . That speaks volumes when we take into account the |
| framework for protection from discrimination has been won , and | my constituents | have shown consistent support for such measures . I congratulate |
| standards officers are aware of many similar examples . In | my constituency | of Luton , South , scare tactics are used to |
| and guns . " n " , " | My constituent | went on to tell me that he had discovered on |
| Is my right hon . Friend aware that many of | my constituents | would like to buy British products in recognition of the |
| many of the early asbestosis claims from Hebden Bridge in | my constituency | might not have succeeded under the proposed 75 per cent |
| At least nine members of the PRS are based in | my constituency | , and the wider south-west is blessed with prolific and |
| that is not regulated properly , with the result that | my constituents | , who have small sums of money available to invest |
| my constituency will get a welcome and much-needed boost . | My constituency | includes Whitchurch comprehensive school , the biggest comprehensive school in |
| In 2010 , I had three jobcentres in | my constituency | . Old Swan was closed by the Minister’s Department at |
| My constituent | , Richard Belmar , has now spent nearly three years | |
| on our streets . n " , " In | my constituency | those officers are declining in number , yet the area |
| Two of | my constituents | , Jeanette Macleod and Margaret Prior , have both received |
| go ahead . There is huge concern about this in | my constituency | and across the north . Was the Prime Minister told |
| a commitment would be warmly welcomed by Corus workers in | my constituency | and elsewhere in the UK . |
| know they are all feeling the pain . Unemployment in | my constituency | has jumped by 16.2 % . We now have the |
| ago , I visited XLP , a charity based in | my constituency | and operating across London to tackle gangs and violent youth |
| should have such a centre in Northampton , so that | my constituents | can get proper access to justice to help them with |
| the moment . n " , " People in | my constituency | tell me that their biggest concerns are about jobs . |
| capital developments are either planned or already in progress . | My constituents | are really seeing the benefits of massive investment in the |
| to consider how the regulator is responding to requests from | my constituents | , who have to wait until the gas supplier receives |
| nearly two decades without being able to contact them . | My constituent | is in litigation against the police , and feels a |
| at hand . Given the high level of interest in | my constituency | , I recently held a listening event that was kindly |
| Wilberforce Freedom fair trade coffee , which is produced in | my constituency | of Hull , so there are commemorative projects of that |
| " , " bring to her attention the situation of | my constituent | George Rolph , who is currently on the 23rd day |
| London , with nearly 13 people chasing every job in | my constituency | . As a result of the cuts in the public |
| . Would he like to visit the children’s centres in | my constituency | that have no children ? |
| long-term unemployment has gone up . More and more of | my constituents | are dependent on food banks that operate in my constituency |
| care that they deserve , particularly during difficult times . | My constituents | will welcome the Bill for its clarity and fairness . |
| " A major supermarket is opening in Cefn Mawr in | my constituency | next Monday , and I welcome that . I welcome |
| for schools . " The outcome was that in | my constituency | and in many areas like it , 30 % of |
| for the people of Welwyn Hatfield , but I know | my constituents | sent me here to win them more jobs , bring |
| not a problem for me or for the people in | my constituency | . In fact , they would probably like to see |
| another young boy has been tragically stabbed to death in | my constituency | . Myron , a talented young rapper , was well |
| to invigorate the UK and radically transform east London and | my constituency | , which I am honoured to serve . As with |
| that a large number of hard-working , two-income families in | my constituency | will be particularly badly hit by any move from a |
| work force . n " , " Thankfully for | my constituents | as customers of the postal system , those differences have |
| Loaves and Fishes food and bank in Easington Lane in | my constituency | . It opened last September and is one of many |
| , which works hand in hand with the police in | my constituency | ? |
| my primary care trust in north-east Derbyshire and dentists in | my constituency | to find a local solution . These reforms coincide with |
| ago . It will come as a huge relief to | my constituents | , who all express the view not only that this |
| hon . Members , with the motorcycle industry and with | my constituents | . My hon . Friend the Member for Rhondda introduced |
| heartening thing about spending time with PCSOs-as I did in | my constituency | on Friday-is the number of people who know their names |
| can be used to improve the job opportunities available to | my constituents | . n " , " Burnley is a mere |
| trade with Europe , and the thousands of people in | my constituency | who have found work through the support of the European |
| years ago there was massive under-enumeration of the population in | my constituency | and my borough of Westminster , as well as in |
| massive part of the cost of living for many of | my constituents | . n " , " Last week in my |
| and communities . I know that only too well from | my constituency | of Wigan . n " , " Although I |
| on salaries of over £ 150,000 . I think that | my constituents | will see the tax as a just tax , and |
| investment , the result of which can be seen throughout | my constituency | through the renewal of children’s play areas , the relaying |
| the local force polices , and although vehicle crime in | my constituency | has increased over the past year , it has reduced |
| tried to follow up on the Prime Minister’s pledge to | my constituents | , his officials said that no help was forthcoming . |
| ready for implementation . It will also delight thousands of | my constituents | who have raised with me the threat of climate change |
| has its main offices in Swansea , where many of | my constituents | work . Why on earth we are here again , |
| in the country . Mr Ash Naghani , one of | my constituents | , told a local newspaper : n " , |
| would do for the 4,000 people who are unemployed in | my constituency | of Leeds West . n " , " One |
| reductions in incomes to ordinary people in benefits . In | my constituency | the average working-age adult is losing £ 560 per year |
| inequalities . n " , " Two wards in | my constituency | have high numbers of people caring for people with stroke |
| seriously , and their perpetrators must be punished properly . | My constituents | and I certainly do not want to see a 50 |
| to rise to my feet again . In Hackney in | my constituency | , we have a new city academy , to which |
| improve such measures as are in place to ensure that | my constituents | benefit as much as possible from the meagre offering they |
| One of | my constituents | recently had his adoption allowance cut because his child received |
| out whether the issues that dominate in those areas of | my constituency | that have most need , most crime and most deprivation |
| n " , " One of the ceramics companies in | my constituency | , Naylor , is a family company , not foreign-owned |
| , such as those at Mount Pleasant sorting office in | my constituency | , are protected ? |
| is certainly one reason why the measure is popular in | my constituency | and elsewhere . The Tories turned the welfare state into |
| is under threat of being withdrawn-a very important issue for | my constituents | . n " , " The petition states that |
| hear that life expectancy in the more deprived parts of | my constituency | is lower than the life expectancy of people living in |
| particular constituency issue-the big threat to the Llanishen reservoir in | my constituency | , and the threat to Cardiff as a whole . |
| be at different schools , miles apart . Many of | my constituents | do not have cars , so it can be almost |
| a well attended housing information event in my constituency ? | My constituents | were engaged in it , and were interested to learn |
| I shall vote for certainty and a better deal for | my constituents | . n " |
| Dundee West ( Jim McGovern ) , a number of | my constituents | have been on the employment and support programme for two |
| aware that , during the summer recess , Honda in | my constituency | announced another 700 jobs to manufacture the new Civic ? |
| pleased to have secured this important debate on behalf of | my constituents | in Cumnock and Girvan . I will shortly present to |
The first implementation used an algorithm developed by Lee & Mimno (2014), implemented in the stm package (Roberts et al., 2018), to estimate the number of topics across all speeches made by female Labour MPs, using the “spectral” method developed by Arora et al. (2013), implemented by Roberts et al. (2018). The resulting topic model has 69 topics, across 81,607 documents and a dictionary of 115,477 words. However, the topic quality with K = 69 is poor, and several topics have poor semantic coherence (see ).
There are several clusters of topics in . For instance, we can see the closeness of Topic 15 (unemployment) and Topic 43 (housing), as both are social issues include discussions of budgets and costs, while Topics 23 (bill amendments) and 16 (education) are very far apart.
Fruchterman-Reingold plot of K69 Network
Coherence of K69 Topic Models
| Topic Number | AWS Speeches | Percent of AWS Speeches | Non-AWS Speeches | Percent of non-AWS Speeches | Male MP Speeches | Percent of Male MP Speeches |
|---|---|---|---|---|---|---|
| Topic 1 | 1,272 | 2.37% | 353 | 1.27% | 3,434 | 2.03% |
| Topic 2 | 334 | 0.62% | 127 | 0.46% | 1,091 | 0.64% |
| Topic 3 | 241 | 0.45% | 71 | 0.25% | 427 | 0.25% |
| Topic 4 | 550 | 1.02% | 133 | 0.48% | 835 | 0.49% |
| Topic 5 | 826 | 1.54% | 206 | 0.74% | 2,452 | 1.45% |
| Topic 6 | 978 | 1.82% | 915 | 3.28% | 4,060 | 2.4% |
| Topic 7 | 648 | 1.21% | 236 | 0.85% | 1,770 | 1.05% |
| Topic 8 | 70 | 0.13% | 25 | 0.09% | 125 | 0.07% |
| Topic 9 | 265 | 0.49% | 309 | 1.11% | 862 | 0.51% |
| Topic 10 | 1,024 | 1.91% | 513 | 1.84% | 1,065 | 0.63% |
| Topic 11 | 940 | 1.75% | 580 | 2.08% | 3,793 | 2.24% |
| Topic 12 | 313 | 0.58% | 319 | 1.14% | 1,309 | 0.77% |
| Topic 13 | 325 | 0.61% | 146 | 0.52% | 1,181 | 0.7% |
| Topic 14 | 1,596 | 2.97% | 461 | 1.65% | 2,885 | 1.7% |
| Topic 15 | 1,386 | 2.58% | 642 | 2.3% | 4,686 | 2.77% |
| Topic 16 | 1,407 | 2.62% | 525 | 1.88% | 3,651 | 2.16% |
| Topic 17 | 3,690 | 6.87% | 1,459 | 5.23% | 19,359 | 11.43% |
| Topic 18 | 1,026 | 1.91% | 847 | 3.04% | 4,760 | 2.81% |
| Topic 19 | 640 | 1.19% | 423 | 1.52% | 2,130 | 1.26% |
| Topic 20 | 872 | 1.62% | 216 | 0.77% | 2,262 | 1.34% |
| Topic 21 | 658 | 1.23% | 363 | 1.3% | 914 | 0.54% |
| Topic 22 | 818 | 1.52% | 439 | 1.57% | 1,965 | 1.16% |
| Topic 23 | 795 | 1.48% | 518 | 1.86% | 3,553 | 2.1% |
| Topic 24 | 385 | 0.72% | 199 | 0.71% | 1,079 | 0.64% |
| Topic 25 | 240 | 0.45% | 74 | 0.27% | 422 | 0.25% |
| Topic 26 | 788 | 1.47% | 200 | 0.72% | 1,738 | 1.03% |
| Topic 27 | 266 | 0.5% | 120 | 0.43% | 1,010 | 0.6% |
| Topic 28 | 847 | 1.58% | 350 | 1.25% | 3,135 | 1.85% |
| Topic 29 | 1,110 | 2.07% | 327 | 1.17% | 944 | 0.56% |
| Topic 30 | 1,132 | 2.11% | 462 | 1.66% | 6,444 | 3.81% |
| Topic 31 | 996 | 1.85% | 975 | 3.49% | 6,077 | 3.59% |
| Topic 32 | 76 | 0.14% | 64 | 0.23% | 335 | 0.2% |
| Topic 33 | 1,238 | 2.31% | 985 | 3.53% | 6,613 | 3.9% |
| Topic 34 | 1,124 | 2.09% | 521 | 1.87% | 3,335 | 1.97% |
| Topic 35 | 650 | 1.21% | 657 | 2.35% | 2,294 | 1.35% |
| Topic 36 | 601 | 1.12% | 154 | 0.55% | 548 | 0.32% |
| Topic 37 | 455 | 0.85% | 194 | 0.7% | 1,554 | 0.92% |
| Topic 38 | 1,246 | 2.32% | 991 | 3.55% | 2,849 | 1.68% |
| Topic 39 | 1,917 | 3.57% | 936 | 3.35% | 7,664 | 4.53% |
| Topic 40 | 848 | 1.58% | 290 | 1.04% | 2,419 | 1.43% |
| Topic 41 | 63 | 0.12% | 40 | 0.14% | 204 | 0.12% |
| Topic 42 | 853 | 1.59% | 590 | 2.11% | 2,016 | 1.19% |
| Topic 43 | 1,344 | 2.5% | 604 | 2.16% | 2,266 | 1.34% |
| Topic 44 | 814 | 1.52% | 288 | 1.03% | 3,005 | 1.77% |
| Topic 45 | 602 | 1.12% | 474 | 1.7% | 1,086 | 0.64% |
| Topic 46 | 709 | 1.32% | 150 | 0.54% | 1,646 | 0.97% |
| Topic 47 | 664 | 1.24% | 245 | 0.88% | 2,992 | 1.77% |
| Topic 48 | 940 | 1.75% | 901 | 3.23% | 3,045 | 1.8% |
| Topic 49 | 835 | 1.55% | 563 | 2.02% | 2,537 | 1.5% |
| Topic 50 | 1,328 | 2.47% | 1,219 | 4.37% | 3,421 | 2.02% |
| Topic 51 | 1,076 | 2% | 323 | 1.16% | 2,453 | 1.45% |
| Topic 52 | 196 | 0.36% | 85 | 0.3% | 758 | 0.45% |
| Topic 53 | 590 | 1.1% | 293 | 1.05% | 746 | 0.44% |
| Topic 54 | 1,057 | 1.97% | 824 | 2.95% | 5,570 | 3.29% |
| Topic 55 | 302 | 0.56% | 157 | 0.56% | 868 | 0.51% |
| Topic 56 | 535 | 1% | 398 | 1.43% | 847 | 0.5% |
| Topic 57 | 656 | 1.22% | 314 | 1.13% | 1,990 | 1.18% |
| Topic 58 | 468 | 0.87% | 182 | 0.65% | 1,125 | 0.66% |
| Topic 59 | 426 | 0.79% | 183 | 0.66% | 700 | 0.41% |
| Topic 60 | 562 | 1.05% | 297 | 1.06% | 1,389 | 0.82% |
| Topic 61 | 86 | 0.16% | 28 | 0.1% | 174 | 0.1% |
| Topic 62 | 550 | 1.02% | 343 | 1.23% | 746 | 0.44% |
| Topic 63 | 690 | 1.28% | 252 | 0.9% | 1,726 | 1.02% |
| Topic 64 | 594 | 1.11% | 244 | 0.87% | 2,247 | 1.33% |
| Topic 65 | 662 | 1.23% | 457 | 1.64% | 907 | 0.54% |
| Topic 66 | 1,493 | 2.78% | 527 | 1.89% | 4,073 | 2.41% |
| Topic 67 | 737 | 1.37% | 451 | 1.62% | 3,237 | 1.91% |
| Topic 68 | 279 | 0.52% | 145 | 0.52% | 547 | 0.32% |
| Topic 69 | 1 | 0% | NA | NA% | NA | NA% |
K69 Pyramid Chart
K69 Bar Chart
The table below shows the ten most common words in each topic, and the ten words with the highest FREX score, a measure that uses a harmonic mean of word exclusivity and topic coherence (Airoldi & Bischof, 2016).
| Topic Number | Top Ten Words | Top Ten FREX |
|---|---|---|
| Topic 1 | secretary, state, tell, ministers, given, today, department, can, confirm, said | secretary, state, confirm, tell, ministers, state’s, minister’s, explain, please, discussions |
| Topic 2 | safety, register, registration, indicated, registered, electoral, risk, risks, number, individual | registration, indicated, hse, canvass, register, gurkhas, safety, dissent, hare, trustee |
| Topic 3 | make, sure, statement, progress, difference, northern, ireland, towards, representations, responsibilities | statement, make, sure, progress, ireland, representations, difference, northern, milton, departmental |
| Topic 4 | debt, water, credit, charges, pay, loan, loans, people, financial, cost | payday, loan, lenders, debts, loans, debt, charges, water, high-cost, creditors |
| Topic 5 | house, committee, parliament, leader, select, motion, parliamentary, debate, scrutiny, business | select, leader, house, motion, committee, backbench, scrutiny, committees, benchers, parliamentary |
| Topic 6 | new, development, work, need, investment, strategy, must, programme, working, also | development, strategy, develop, project, regional, projects, partnership, together, developed, build |
| Topic 7 | road, petition, residents, car, vehicles, petitioners, dogs, roads, site, house | petitioners, lap-dancing, petition, dogs, dog, pedestrians, cycling, declares, drivers, accidents |
| Topic 8 | important, agree, welcome, country, making, particularly, thank, part, makes, good | agree, welcome, important, absolutely, makes, making, friend’s, thank, particularly, giving |
| Topic 9 | companies, market, company, competition, energy, consumers, prices, price, consumer, customers | competition, companies, market, wholesale, suppliers, company, regulator, ofgem, supplier, consumers |
| Topic 10 | women, men, equality, women’s, discrimination, rights, gender, equal, woman, marriage | gender, bishops, transgender, women’s, women, abortion, same-sex, marriage, equality, gay |
| Topic 11 | energy, climate, fuel, change, green, carbon, emissions, gas, environmental, industry | renewables, solar, insulation, feed-in, biofuels, greenhouse, dioxide, kyoto, carbon, climate |
| Topic 12 | office, post, offices, royal, service, closure, mail, services, network, christmas | offices, mail, sub-post, post, sub-postmasters, closures, consignia, swindon, closure, office |
| Topic 13 | mr, north, south, east, west, spoke, friends, birmingham, talked, central | ealing, spoke, dorset, lothian, ayrshire, glasgow, chris, southwark, pontefract, birmingham |
| Topic 14 | pension, scheme, benefit, pensions, benefits, pensioners, system, credit, allowance, income | pension, esa, pensions, claimants, retirement, pip, pensioners, incapacity, dwp, means-testing |
| Topic 15 | economy, jobs, economic, growth, unemployment, country, investment, chancellor, budget, crisis | unemployment, recession, growth, economy, obr, deficit, inflation, economic, forecast, borrowing |
| Topic 16 | schools, school, education, children, teachers, parents, pupils, educational, special, primary | academies, pupil, grammar, schools, pupils, teachers, ofsted, school, teacher, sen |
| Topic 17 | want, say, one, think, know, need, us, get, go, see | think, say, things, want, something, saying, going, lot, really, go |
| Topic 18 | review, report, commission, independent, process, recommendations, inquiry, also, system, standards | recommendations, inquiry, panel, audit, independent, recommendation, reviews, fsa, complaints, review |
| Topic 19 | business, businesses, small, financial, bank, banks, insurance, rates, industry, enterprise | smes, medium-sized, businesses, bank, enterprises, enterprise, banking, rbs, business, rock |
| Topic 20 | wales, industry, welsh, north-east, england, assembly, constituency, jobs, manufacturing, uk | welsh, wales, steel, cardiff, north-east, assembly, visteon, newcastle, manufacturing, tyneside |
| Topic 21 | care, services, social, mental, need, health, home, provision, service, older | mental, care, social, elderly, older, advocacy, services, residential, palliative, discharges |
| Topic 22 | pay, work, workers, employment, working, wage, minimum, employers, paid, national | wage, workers, zero-hours, employees, paternity, employer, minimum, employers, employment, workplace |
| Topic 23 | amendment, clause, amendments, new, 1, lords, section, 2, act, clauses | amendment, nos, insert, subsection, clause, amendments, clauses, section, lords, schedule |
| Topic 24 | report, last, since, said, received, published, year, following, official, end | march, vol, official, january, july, november, published, december, june, october |
| Topic 25 | made, clear, impact, decision, changes, recent, assessment, decisions, effect, proposed | made, decision, assessment, clear, decisions, impact, implications, recent, changes, effect |
| Topic 26 | funding, cuts, fund, cut, budget, grant, spending, bbc, review, flood | flood, funding, bbc, formula, grant, flooding, floods, cumbria, lottery, grants |
| Topic 27 | money, spent, extra, spend, liberal, cost, spending, value, opposition, tory | money, spent, liberal, spend, democrats, tories, tory, lib, democrat, conservatives |
| Topic 28 | constituency, great, community, proud, many, sport, one, also, world, new | maiden, arts, football, museum, museums, sport, olympic, games, sports, heritage |
| Topic 29 | families, child, poverty, children, parents, work, credit, working, family, living | lone, poverty, childcare, families, low-income, child, nursery, four-year-olds, nurseries, joseph |
| Topic 30 | party, conservative, vote, parliament, political, election, labour, parties, scottish, elected | party, vote, voting, conservative, party’s, voters, election, voted, votes, politics |
| Topic 31 | point, can, may, issue, take, however, whether, matter, understand, consider | matter, point, understand, consider, certainly, accept, possible, issue, course, happy |
| Topic 32 | member, said, lady, mentioned, raised, comments, speech, referred, points, remarks | member, lady, comments, remarks, bromley, interesting, chislehurst, pointed, front-bench, mentioned |
| Topic 33 | european, uk, eu, countries, united, union, europe, states, british, trade | accession, enlargement, wto, lisbon, treaty, eu, doha, european, negotiations, brexit |
| Topic 34 | education, skills, young, training, students, university, college, higher, science, apprenticeships | ema, fe, students, apprenticeship, universities, qualifications, apprenticeships, graduates, vocational, courses |
| Topic 35 | local, authorities, authority, planning, community, communities, councils, area, guidance, system | authorities, local, authority, planning, councils, councillors, locally, guidance, localism, communities |
| Topic 36 | disabled, carers, disability, support, disabilities, needs, caring, autism, learning, can | carers, autism, autistic, disabled, disabilities, disability, dementia, carer, caring, deaf |
| Topic 37 | environment, marine, fishing, sea, industry, natural, fish, countryside, rural, fisheries | fishermen, cod, forestry, biodiversity, habitats, mmo, fishing, fish, cfp, fisheries |
| Topic 38 | justice, court, violence, victims, cases, criminal, domestic, courts, prison, offence | attorney-general, defendants, defendant, prison, prosecutors, solicitor-general, stalking, prosecutor, prisons, prosecution |
| Topic 39 | international, foreign, rights, human, peace, un, conflict, world, aid, war | israel, palestinian, israeli, gaza, sri, zimbabwe, iran, yemen, hamas, palestinians |
| Topic 40 | day, family, never, told, families, life, happened, constituent, man, went | man, died, son, story, stories, hillsborough, tragedy, daughter, husband, angry |
| Topic 41 | proposals, future, forward, consultation, plans, meet, paper, current, discuss, bring | proposals, consultation, paper, plans, forward, discuss, white, proposal, meet, implement |
| Topic 42 | behaviour, crime, antisocial, alcohol, young, drugs, drug, problem, use, tackle | antisocial, asbos, alcohol, alcohol-related, binge, psychoactive, drinking, fireworks, behaviour, graffiti |
| Topic 43 | housing, homes, social, affordable, private, home, accommodation, rent, need, properties | housing, tenants, rented, tenancies, homelessness, leasehold, landlords, rents, properties, leaseholders |
| Topic 44 | question, order, mr, put, asked, answer, questions, ask, speaker, time | question, answer, questions, speaker, asked, deputy, answers, order, apologise, read |
| Topic 45 | research, cancer, treatment, medical, condition, screening, disease, can, patients, use | embryos, prostate, cervical, hepatitis, cloning, transplant, embryo, fertilisation, embryonic, endometriosis |
| Topic 46 | online, internet, farmers, animals, digital, animal, broadband, sites, tickets, technology | cull, badgers, badger, fur, bovine, mink, culling, circuses, touts, snares |
| Topic 47 | defence, forces, armed, plymouth, personnel, service, military, army, nuclear, royal | mod, naval, hms, submarines, dockyard, veterans, armed, plymouth, covenant, personnel |
| Topic 48 | information, home, security, data, immigration, control, orders, system, terrorism, appeal | extradition, tpims, sia, warrant, detention, checks, tpim, terrorism, intercept, identity |
| Topic 49 | police, officers, crime, policing, home, force, service, forces, officer, chief | constable, constables, officers, policing, police, soca, ipcc, constabulary, pcsos, hmic |
| Topic 50 | nhs, hospital, patients, health, services, hospitals, care, service, trust, trusts | dentists, dentistry, pharmacies, pct, nhs, hospitals, hospital, dental, trusts, patients |
| Topic 51 | tax, budget, cut, chancellor, cuts, rate, income, vat, benefit, hit | 50p, vat, millionaires, hit, tax, allowances, credits, richest, chancellor, ifs |
| Topic 52 | years, now, two, time, first, three, past, one, months, ago | years, three, months, ago, two, past, weeks, five, four, now |
| Topic 53 | staff, doctors, emergency, medical, service, training, nurses, royal, junior, ambulance | ambulance, junior, staffing, doctors, halifax, posts, nurses, fss, staff, cpr |
| Topic 54 | bill, legislation, act, law, rights, provisions, powers, regulations, place, believe | bill, legislation, bill’s, provisions, passage, regulations, legislative, draft, statute, definition |
| Topic 55 | public, sector, private, organisations, service, voluntary, services, society, community, organisation | public, voluntary, organisations, sector, private, co-operative, volunteering, volunteers, volunteer, co-operatives |
| Topic 56 | health, national, inequalities, programme, suicide, disease, department, prevention, among, risk | flu, hiv, pandemic, inequalities, infections, suicide, mortality, infection, mrsa, vaccine |
| Topic 57 | council, london, areas, city, area, constituency, centre, rural, county, liverpool | county, mayor, borough, cities, liverpool, city, regeneration, council’s, london, towns |
| Topic 58 | advice, legal, cases, civil, hull, aid, case, compensation, claims, service | hull, tribunal, legal, compensation, solicitors, advice, concentrix, servants, lawyers, tribunals |
| Topic 59 | people, work, many, young, get, people’s, can, help, lives, job | people, people’s, get, getting, work, young, jobcentre, lives, youth, find |
| Topic 60 | tax, revenue, relief, duty, uk, avoidance, hmrc, charities, companies, taxation | evasion, hmrc, gaar, avoidance, inland, stamp, revenue, relief, gift, dependencies |
| Topic 61 | government, government’s, policy, labour, previous, scotland, scottish, commitment, policies, coalition | government, previous, policy, government’s, scotland, coalition, scottish, labour, disappointing, administrations |
| Topic 62 | trafficking, home, uk, asylum, refugees, immigration, country, human, migration, britain | trafficking, slavery, trafficked, sierra, leone, slave, dubs, fgm, yarl’s, wilberforce |
| Topic 63 | food, products, industry, smoking, advertising, tobacco, ban, product, standards, shops | gambling, betting, sunbed, tobacco, cocoa, meat, supermarkets, labelling, retailers, packaging |
| Topic 64 | members, debate, many, issues, also, today, heard, opportunity, hope, issue | members, debate, heard, speak, sides, issues, hear, opportunity, listened, pleased |
| Topic 65 | children, child, parents, young, children’s, family, contact, vulnerable, adoption, abuse | csa, adopters, adoption, child’s, cafcass, looked-after, children’s, children, safeguarding, barred |
| Topic 66 | transport, rail, bus, services, line, travel, train, network, passengers, london | rail, passengers, passenger, heathrow, hs2, freight, high-speed, crossrail, airlines, runway |
| Topic 67 | year, million, number, increase, figures, increased, billion, 1, average, cost | million, figures, figure, increased, increase, compared, year, total, fallen, estimates |
| Topic 68 | support, ensure, can, help, aware, taking, take, provide, action, continue | aware, ensure, support, taking, steps, continue, help, action, assure, encourage |
| Topic 69 | deal, recently, new, can, lack, great, concern, done, move, given | deal, recently, lack, elsewhere, concern, great, improved, offered, done, new |
## A topic model with 69 topics, 81607 documents and a 115477 word dictionary.
## Topic 1 Top Words:
## Highest Prob: secretary, state, tell, ministers, given, today, department
## FREX: secretary, state, confirm, tell, ministers, state's, minister's
## Lift: dhar, lowell, qatada's, #nationalistsconfused, 1135, 2.5bn, 36,500
## Score: secretary, state, confirm, state's, tell, ministers, department
## Topic 2 Top Words:
## Highest Prob: safety, register, registration, indicated, registered, electoral, risk
## FREX: registration, indicated, hse, canvass, register, gurkhas, safety
## Lift: aps, hse, 10-litre, 13a, 14,940, 1760, 1867
## Score: safety, registration, register, electoral, indicated, registered, hse
## Topic 3 Top Words:
## Highest Prob: make, sure, statement, progress, difference, northern, ireland
## FREX: statement, make, sure, progress, ireland, representations, difference
## Lift: 101269, 101548, 102938, 103414, 106583, 107305, 109413
## Score: make, statement, progress, sure, ireland, northern, milton
## Topic 4 Top Words:
## Highest Prob: debt, water, credit, charges, pay, loan, loans
## FREX: payday, loan, lenders, debts, loans, debt, charges
## Lift: 1,021, 1,025, 1,106, 1,189, 1,273, 1,385, 1,413
## Score: debt, water, payday, loan, loans, lenders, credit
## Topic 5 Top Words:
## Highest Prob: house, committee, parliament, leader, select, motion, parliamentary
## FREX: select, leader, house, motion, committee, backbench, scrutiny
## Lift: e-petitions, praying-against, sherlock, wednesdays, sittings, thursdays, 10,000-signature
## Score: committee, house, leader, select, scrutiny, parliament, motion
## Topic 6 Top Words:
## Highest Prob: new, development, work, need, investment, strategy, must
## FREX: development, strategy, develop, project, regional, projects, partnership
## Lift: #1,150, 1.245, 1.875, 128406, 131,000, 18-the, 2002-around
## Score: development, regional, investment, strategy, infrastructure, projects, work
## Topic 7 Top Words:
## Highest Prob: road, petition, residents, car, vehicles, petitioners, dogs
## FREX: petitioners, lap-dancing, petition, dogs, dog, pedestrians, cycling
## Lift: 0.037, 0.044, 0fficial, 1,042, 1,072, 1,108, 1,122
## Score: petitioners, petition, dogs, road, residents, dog, declares
## Topic 8 Top Words:
## Highest Prob: important, agree, welcome, country, making, particularly, thank
## FREX: agree, welcome, important, absolutely, makes, making, friend's
## Lift: adequacies, and-importantly-much, ayse, ballantine, bobtail-and, bostrom, bucketfuls
## Score: agree, important, thank, welcome, friend's, absolutely, country
## Topic 9 Top Words:
## Highest Prob: companies, market, company, competition, energy, consumers, prices
## FREX: competition, companies, market, wholesale, suppliers, company, regulator
## Lift: 1,105, 1,345, ashington, boakye, nord, over-charging, price-fixing
## Score: companies, consumers, energy, market, company, prices, competition
## Topic 10 Top Words:
## Highest Prob: women, men, equality, women's, discrimination, rights, gender
## FREX: gender, bishops, transgender, women's, women, abortion, same-sex
## Lift: balmforth, bpas, celebrants, cohabitants, jessy, msafiri, natal
## Score: women, women's, equality, men, gender, discrimination, marriage
## Topic 11 Top Words:
## Highest Prob: energy, climate, fuel, change, green, carbon, emissions
## FREX: renewables, solar, insulation, feed-in, biofuels, greenhouse, dioxide
## Lift: fossil, #2,500, #solar, 1-are, 1,129, 1,214, 1,343
## Score: energy, fuel, carbon, emissions, climate, renewable, renewables
## Topic 12 Top Words:
## Highest Prob: office, post, offices, royal, service, closure, mail
## FREX: offices, mail, sub-post, post, sub-postmasters, closures, consignia
## Lift: #1.8, #210, #450, 1,001, 1,352, 1,639, 1,827
## Score: post, offices, office, mail, closure, postal, sub-post
## Topic 13 Top Words:
## Highest Prob: mr, north, south, east, west, spoke, friends
## FREX: ealing, spoke, dorset, lothian, ayrshire, glasgow, chris
## Lift: argos's, blackford, cairns's, clemency, marxist, no2id, 0.66
## Score: mr, east, north, south, west, spoke, birmingham
## Topic 14 Top Words:
## Highest Prob: pension, scheme, benefit, pensions, benefits, pensioners, system
## FREX: pension, esa, pensions, claimants, retirement, pip, pensioners
## Lift: means-testing, #20,000, #400, 0º, 1,052, 1,366, 1,482
## Score: pension, pensions, pensioners, allowance, scheme, retirement, credit
## Topic 15 Top Words:
## Highest Prob: economy, jobs, economic, growth, unemployment, country, investment
## FREX: unemployment, recession, growth, economy, obr, deficit, inflation
## Lift: 0.76, 0.83, 1,196, 1,319, 1.04, 1.32, 10-about
## Score: economy, jobs, unemployment, growth, economic, recession, chancellor
## Topic 16 Top Words:
## Highest Prob: schools, school, education, children, teachers, parents, pupils
## FREX: academies, pupil, grammar, schools, pupils, teachers, ofsted
## Lift: sen2, 11-plus, leas, pupil, 000-to, 1-regardless, 1,000-pupil
## Score: schools, school, teachers, pupils, children, education, parents
## Topic 17 Top Words:
## Highest Prob: want, say, one, think, know, need, us
## FREX: think, say, things, want, something, saying, going
## Lift: about-part, arguing-i, beneficial-we, career-many, cause-the, clam, compatriot
## Score: think, want, get, say, things, going, us
## Topic 18 Top Words:
## Highest Prob: review, report, commission, independent, process, recommendations, inquiry
## FREX: recommendations, inquiry, panel, audit, independent, recommendation, reviews
## Lift: bourn, cag's, clarke's, clowes, dg, emag, equitable's
## Score: fsa, inquiry, review, commission, recommendations, report, independent
## Topic 19 Top Words:
## Highest Prob: business, businesses, small, financial, bank, banks, insurance
## FREX: smes, medium-sized, businesses, bank, enterprises, enterprise, banking
## Lift: 0.21, 0.84, 1,034, 1,130, 10-fold, 1130, 12.19
## Score: businesses, business, bank, banks, banking, insurance, small
## Topic 20 Top Words:
## Highest Prob: wales, industry, welsh, north-east, england, assembly, constituency
## FREX: welsh, wales, steel, cardiff, north-east, assembly, visteon
## Lift: a55, angharad, bethesda, co-investment, dewhirst, dogger, gorge
## Score: wales, welsh, assembly, manufacturing, steel, north-east, yorkshire
## Topic 21 Top Words:
## Highest Prob: care, services, social, mental, need, health, home
## FREX: mental, care, social, elderly, older, advocacy, services
## Lift: #900,000, 1,051, 1,312, 10,758, 104962, 1089, 13,198
## Score: care, mental, services, social, health, older, homes
## Topic 22 Top Words:
## Highest Prob: pay, work, workers, employment, working, wage, minimum
## FREX: wage, workers, zero-hours, employees, paternity, employer, minimum
## Lift: 6.70, 8.20, 85p, awb, e-balloting, hannett, increments
## Score: wage, workers, employers, employment, pay, employees, minimum
## Topic 23 Top Words:
## Highest Prob: amendment, clause, amendments, new, 1, lords, section
## FREX: amendment, nos, insert, subsection, clause, amendments, clauses
## Lift: 153a, 22a, 287, 50b, 51b, counter-notice, insured's
## Score: clause, amendment, amendments, lords, nos, insert, subsection
## Topic 24 Top Words:
## Highest Prob: report, last, since, said, received, published, year
## FREX: march, vol, official, january, july, november, published
## Lift: 1-2ws, 1,033, 1,099, 1,124,818, 1,337, 1,368,186, 1,595
## Score: report, official, vol, published, march, april, november
## Topic 25 Top Words:
## Highest Prob: made, clear, impact, decision, changes, recent, assessment
## FREX: made, decision, assessment, clear, decisions, impact, implications
## Lift: 104963, 107312, 116,400, 125214, 125828, 125830, 126370
## Score: made, assessment, impact, changes, decision, decisions, clear
## Topic 26 Top Words:
## Highest Prob: funding, cuts, fund, cut, budget, grant, spending
## FREX: flood, funding, bbc, formula, grant, flooding, floods
## Lift: #10.89, #12, #3.3, #bbcdiversity, 1,027, 1,536-will, 1,546
## Score: funding, cuts, flood, bbc, budget, spending, flooding
## Topic 27 Top Words:
## Highest Prob: money, spent, extra, spend, liberal, cost, spending
## FREX: money, spent, liberal, spend, democrats, tories, tory
## Lift: 1-but, 10,309.63, 1228, 158.8, 1763, 18-that, 1979-80
## Score: money, liberal, tory, democrats, conservatives, tories, spending
## Topic 28 Top Words:
## Highest Prob: constituency, great, community, proud, many, sport, one
## FREX: maiden, arts, football, museum, museums, sport, olympic
## Lift: 0.27, 0.51, 1,084, 1,126, 1,468, 1,580-plus, 1,983
## Score: arts, sport, museum, maiden, heritage, football, constituency
## Topic 29 Top Words:
## Highest Prob: families, child, poverty, children, parents, work, credit
## FREX: lone, poverty, childcare, families, low-income, child, nursery
## Lift: 1,000-discriminates, 1,000-not, 1,080, 1,142,600, 1,170, 1,390, 1,664
## Score: poverty, child, families, children, parents, credit, lone
## Topic 30 Top Words:
## Highest Prob: party, conservative, vote, parliament, political, election, labour
## FREX: party, vote, voting, conservative, party's, voters, election
## Lift: alphabetical, gentry, olga, one-party, randomisation, 1,166, 1,294
## Score: party, conservative, vote, scottish, election, elections, political
## Topic 31 Top Words:
## Highest Prob: point, can, may, issue, take, however, whether
## FREX: matter, point, understand, consider, certainly, accept, possible
## Lift: 450,000-in, advised-by, backslid, bill-albeit, bizarre-but, can-enable, cases-roughly
## Score: point, matter, issue, gentleman's, consider, shall, whether
## Topic 32 Top Words:
## Highest Prob: member, said, lady, mentioned, raised, comments, speech
## FREX: member, lady, comments, remarks, bromley, interesting, chislehurst
## Lift: 12.20, 130b, 1991-forcing, 2,784, 54,000-worth, achieved-is, arguments-and
## Score: member, lady, comments, said, speech, raised, points
## Topic 33 Top Words:
## Highest Prob: european, uk, eu, countries, united, union, europe
## FREX: accession, enlargement, wto, lisbon, treaty, eu, doha
## Lift: 13652, 13653, 13654, 1707, balkan, barnier, blackmailing
## Score: eu, european, countries, union, treaty, europe, trade
## Topic 34 Top Words:
## Highest Prob: education, skills, young, training, students, university, college
## FREX: ema, fe, students, apprenticeship, universities, qualifications, apprenticeships
## Lift: ema, #ne, 1,188, 1,308, 1,555-what, 1,740, 1,803
## Score: students, education, young, skills, apprenticeships, training, universities
## Topic 35 Top Words:
## Highest Prob: local, authorities, authority, planning, community, communities, councils
## FREX: authorities, local, authority, planning, councils, councillors, locally
## Lift: achcew, central-local, laa, lsp, maas, observances, place-shaping
## Score: local, authorities, authority, councils, planning, communities, community
## Topic 36 Top Words:
## Highest Prob: disabled, carers, disability, support, disabilities, needs, caring
## FREX: carers, autism, autistic, disabled, disabilities, disability, dementia
## Lift: autism, rnib, #185, #85, #hellomynameis, 1,400-one, 10-person
## Score: carers, disabled, disability, autism, disabilities, caring, dementia
## Topic 37 Top Words:
## Highest Prob: environment, marine, fishing, sea, industry, natural, fish
## FREX: fishermen, cod, forestry, biodiversity, habitats, mmo, fishing
## Lift: aquaculture, arable, bee-friendly, biodiversity, birdlife, bycatch, caterpillar
## Score: marine, fishing, fishermen, fish, fisheries, wildlife, conservation
## Topic 38 Top Words:
## Highest Prob: justice, court, violence, victims, cases, criminal, domestic
## FREX: attorney-general, defendants, defendant, prison, prosecutors, solicitor-general, stalking
## Lift: #9, 0.08, 0.48, 1,046, 1,237, 10,544, 10.15
## Score: violence, prison, court, offence, criminal, rape, victims
## Topic 39 Top Words:
## Highest Prob: international, foreign, rights, human, peace, un, conflict
## FREX: israel, palestinian, israeli, gaza, sri, zimbabwe, iran
## Lift: lankan, saddam, #aleppo, 1,010, 1,476, 1,591, 1224
## Score: un, israel, syria, humanitarian, palestinian, israeli, iraq
## Topic 40 Top Words:
## Highest Prob: day, family, never, told, families, life, happened
## FREX: man, died, son, story, stories, hillsborough, tragedy
## Lift: 10,000-seat, 12-inch, 1234, 1519-20, 1635, 1710, 174,995
## Score: families, holocaust, family, constituent, man, died, mother
## Topic 41 Top Words:
## Highest Prob: proposals, future, forward, consultation, plans, meet, paper
## FREX: proposals, consultation, paper, plans, forward, discuss, white
## Lift: 10.42, 107910, 109648, 114061, 119621, 141605, 141607
## Score: proposals, consultation, plans, future, forward, paper, white
## Topic 42 Top Words:
## Highest Prob: behaviour, crime, antisocial, alcohol, young, drugs, drug
## FREX: antisocial, asbos, alcohol, alcohol-related, binge, psychoactive, drinking
## Lift: acquisitive, addaction, auto, bailes, crawlers, ghb, gilpin
## Score: antisocial, crime, behaviour, alcohol, drug, drugs, cannabis
## Topic 43 Top Words:
## Highest Prob: housing, homes, social, affordable, private, home, accommodation
## FREX: housing, tenants, rented, tenancies, homelessness, leasehold, landlords
## Lift: one-for-one, one-bedroom, rented, right-to-buy, #19, #21.5, #28.5
## Score: housing, homes, tenants, rented, rent, landlords, affordable
## Topic 44 Top Words:
## Highest Prob: question, order, mr, put, asked, answer, questions
## FREX: question, answer, questions, speaker, asked, deputy, answers
## Lift: 11.00, 11.57, 12.26, 1223, 1232, 1412, 1555-56
## Score: question, speaker, mr, answer, deputy, order, questions
## Topic 45 Top Words:
## Highest Prob: research, cancer, treatment, medical, condition, screening, disease
## FREX: embryos, prostate, cervical, hepatitis, cloning, transplant, embryo
## Lift: abnormalities, cystic, embryo, fertilisation, marrow, @cfaware, #500
## Score: cancer, patients, embryos, screening, treatment, tissue, breast
## Topic 46 Top Words:
## Highest Prob: online, internet, farmers, animals, digital, animal, broadband
## FREX: cull, badgers, badger, fur, bovine, mink, culling
## Lift: culling, @daisydumble, @donna_smiley, @jimspin, @leamingtonsbc, @maggieannehayes, @nhconsortium
## Score: farmers, animals, internet, cull, animal, online, badgers
## Topic 47 Top Words:
## Highest Prob: defence, forces, armed, plymouth, personnel, service, military
## FREX: mod, naval, hms, submarines, dockyard, veterans, armed
## Lift: hms, submarine, submarines, 1,000-people, 1,625, 1,705, 10-3
## Score: defence, armed, forces, plymouth, military, personnel, mod
## Topic 48 Top Words:
## Highest Prob: information, home, security, data, immigration, control, orders
## FREX: extradition, tpims, sia, warrant, detention, checks, tpim
## Lift: carlile's, carlile, sia, 10-month, 10,410, 10,500-for, 10.45
## Score: immigration, terrorism, detention, terrorist, tpims, home, security
## Topic 49 Top Words:
## Highest Prob: police, officers, crime, policing, home, force, service
## FREX: constable, constables, officers, policing, police, soca, ipcc
## Lift: 2003-morecambe, 9,650, a19s, ashleys, bigg, bounties, ckp
## Score: police, officers, policing, crime, forces, constable, neighbourhood
## Topic 50 Top Words:
## Highest Prob: nhs, hospital, patients, health, services, hospitals, care
## FREX: dentists, dentistry, pharmacies, pct, nhs, hospitals, hospital
## Lift: acos, bequest, bernstein, bodmin, catto, cayton, chailey
## Score: nhs, patients, hospital, health, patient, hospitals, care
## Topic 51 Top Words:
## Highest Prob: tax, budget, cut, chancellor, cuts, rate, income
## FREX: 50p, vat, millionaires, hit, tax, allowances, credits
## Lift: #840, 0.76p, 1,003, 1,009, 1,226, 1,275, 1,296
## Score: tax, vat, budget, credits, chancellor, cuts, income
## Topic 52 Top Words:
## Highest Prob: years, now, two, time, first, three, past
## FREX: years, three, months, ago, two, past, weeks
## Lift: 10-week-old, 10,616, 11-month, 11-point, 1758, 196b, 63,500
## Score: years, months, two, ago, three, past, weeks
## Topic 53 Top Words:
## Highest Prob: staff, doctors, emergency, medical, service, training, nurses
## FREX: ambulance, junior, staffing, doctors, halifax, posts, nurses
## Lift: #i'm, 03, 1-who, 1,454, 1,631, 10,000-strong, 10,000-with
## Score: staff, doctors, ambulance, nurses, medical, emergency, junior
## Topic 54 Top Words:
## Highest Prob: bill, legislation, act, law, rights, provisions, powers
## FREX: bill, legislation, bill's, provisions, passage, regulations, legislative
## Lift: 1865, 1990s-when, 1998-it, 19may2000, 2003-largely, 2005-have, 29-year
## Score: bill, legislation, provisions, rights, law, powers, regulations
## Topic 55 Top Words:
## Highest Prob: public, sector, private, organisations, service, voluntary, services
## FREX: public, voluntary, organisations, sector, private, co-operative, volunteering
## Lift: 1844, af, carpetbaggers, nebulous, puk, 1075, 170-year
## Score: public, sector, private, voluntary, organisations, service, services
## Topic 56 Top Words:
## Highest Prob: health, national, inequalities, programme, suicide, disease, department
## FREX: flu, hiv, pandemic, inequalities, infections, suicide, mortality
## Lift: kirkley, lowestoft, nihr, acupuncture, influenza, #148, #3.6
## Score: health, vaccine, flu, inequalities, hiv, infection, suicide
## Topic 57 Top Words:
## Highest Prob: council, london, areas, city, area, constituency, centre
## FREX: county, mayor, borough, cities, liverpool, city, regeneration
## Lift: #12,000, #14.4, #356, #38, #5,000, #500,000, #66.6
## Score: london, council, city, regeneration, county, rural, borough
## Topic 58 Top Words:
## Highest Prob: advice, legal, cases, civil, hull, aid, case
## FREX: hull, tribunal, legal, compensation, solicitors, advice, concentrix
## Lift: 0300, 1,997, 1.148, 1.4m, 112.8, 1147, 128,687
## Score: legal, advice, hull, aid, compensation, civil, tribunal
## Topic 59 Top Words:
## Highest Prob: people, work, many, young, get, people's, can
## FREX: people, people's, get, getting, work, young, jobcentre
## Lift: 2,425, 294,488, 3,699, 37,290, 5,320, 50-to-64, 75589
## Score: people, young, work, get, youth, many, people's
## Topic 60 Top Words:
## Highest Prob: tax, revenue, relief, duty, uk, avoidance, hmrc
## FREX: evasion, hmrc, gaar, avoidance, inland, stamp, revenue
## Lift: 1,643, 3.12, 32.2, 44a, 80g, aaronson, aat
## Score: tax, hmrc, avoidance, revenue, relief, evasion, territories
## Topic 61 Top Words:
## Highest Prob: government, government's, policy, labour, previous, scotland, scottish
## FREX: government, previous, policy, government's, scotland, coalition, scottish
## Lift: 2005-perhaps, 80994, actually-that, agency-when, agenda-access, alloway, aware-in
## Score: government, scotland, scottish, labour, policy, government's, previous
## Topic 62 Top Words:
## Highest Prob: trafficking, home, uk, asylum, refugees, immigration, country
## FREX: trafficking, slavery, trafficked, sierra, leone, slave, dubs
## Lift: #7, 0.025, 1-yes, 1,060, 1,483, 1,746, 1.123
## Score: trafficking, refugees, asylum, slavery, trafficked, immigration, sierra
## Topic 63 Top Words:
## Highest Prob: food, products, industry, smoking, advertising, tobacco, ban
## FREX: gambling, betting, sunbed, tobacco, cocoa, meat, supermarkets
## Lift: 0.7p, 00, 0157, 1,000-almost, 1,032, 1,200-i, 1,666
## Score: food, smoking, products, tobacco, advertising, gambling, industry
## Topic 64 Top Words:
## Highest Prob: members, debate, many, issues, also, today, heard
## FREX: members, debate, heard, speak, sides, issues, hear
## Lift: noakes, 6.47pm, accept-or, analysis-but, aspect-listening-is, bunfight, called-making
## Score: members, debate, issues, many, opposition, heard, constituents
## Topic 65 Top Words:
## Highest Prob: children, child, parents, young, children's, family, contact
## FREX: csa, adopters, adoption, child's, cafcass, looked-after, children's
## Lift: csa, @mandatenow, 10-month-old, 10-went, 12j, 150765, 16-only
## Score: children, child, parents, young, children's, adoption, child's
## Topic 66 Top Words:
## Highest Prob: transport, rail, bus, services, line, travel, train
## FREX: rail, passengers, passenger, heathrow, hs2, freight, high-speed
## Lift: 12-car, 15.15, 50.1, adtranz, anti-icing, bahn, chilterns
## Score: rail, transport, bus, passengers, fares, trains, hs2
## Topic 67 Top Words:
## Highest Prob: year, million, number, increase, figures, increased, billion
## FREX: million, figures, figure, increased, increase, compared, year
## Lift: #112, #3,850, #84.3, #87.2, 1,249, 102269, 122.9
## Score: million, year, billion, increase, figures, average, increased
## Topic 68 Top Words:
## Highest Prob: support, ensure, can, help, aware, taking, take
## FREX: aware, ensure, support, taking, steps, continue, help
## Lift: 103684, 103965, 107320, 111532, 112584, 113698, 117890
## Score: support, ensure, steps, aware, help, taking, department
## Topic 69 Top Words:
## Highest Prob: deal, recently, new, can, lack, great, concern
## FREX: deal, recently, lack, elsewhere, concern, great, improved
## Lift: 2004-a, 721,000, added-gva-per, age-of, centres-jacs-which, employment-can, index-the
## Score: deal, recently, new, worktrack, lack, can, great
##
## Call:
## estimateEffect(formula = 1:69 ~ short_list, stmobj = topic_model2,
## metadata = lab_corpus_fem_stm$meta, uncertainty = "Global")
##
##
## Topic 1:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0170435 0.0002879 59.20 <0.0000000000000002 ***
## short_listTRUE 0.0069026 0.0003496 19.75 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 2:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0058955 0.0002420 24.358 <0.0000000000000002 ***
## short_listTRUE 0.0007062 0.0003050 2.315 0.0206 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 3:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0087144 0.0001127 77.309 <0.0000000000000002 ***
## short_listTRUE 0.0002131 0.0001475 1.445 0.148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 4:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0064743 0.0003071 21.08 <0.0000000000000002 ***
## short_listTRUE 0.0036202 0.0003918 9.24 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 5:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0120624 0.0002519 47.88 <0.0000000000000002 ***
## short_listTRUE 0.0035421 0.0003303 10.72 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 6:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0313831 0.0004291 73.14 <0.0000000000000002 ***
## short_listTRUE -0.0093831 0.0004957 -18.93 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 7:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0081985 0.0003833 21.390 < 0.0000000000000002 ***
## short_listTRUE 0.0024229 0.0004801 5.047 0.00000045 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 8:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0155977 0.0001354 115.222 < 0.0000000000000002 ***
## short_listTRUE -0.0006674 0.0001470 -4.542 0.00000559 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 9:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0106919 0.0002692 39.718 < 0.0000000000000002 ***
## short_listTRUE -0.0025219 0.0003483 -7.241 0.00000000000045 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 10:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0147989 0.0004500 32.885 <0.0000000000000002 ***
## short_listTRUE 0.0002626 0.0005572 0.471 0.637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 11:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0131078 0.0004357 30.084 <0.0000000000000002 ***
## short_listTRUE -0.0009014 0.0005203 -1.733 0.0832 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 12:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0100322 0.0003276 30.62 < 0.0000000000000002 ***
## short_listTRUE -0.0026446 0.0003762 -7.03 0.00000000000208 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 13:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0084706 0.0002257 37.532 < 0.0000000000000002 ***
## short_listTRUE 0.0015075 0.0002922 5.159 0.000000249 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 14:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0133846 0.0004574 29.26 <0.0000000000000002 ***
## short_listTRUE 0.0060865 0.0005480 11.11 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 15:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0169555 0.0004249 39.905 < 0.0000000000000002 ***
## short_listTRUE 0.0029760 0.0005174 5.752 0.00000000883 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 16:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0135136 0.0005241 25.786 < 0.0000000000000002 ***
## short_listTRUE 0.0032587 0.0006744 4.832 0.00000135 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 17:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0425046 0.0003183 133.546 <0.0000000000000002 ***
## short_listTRUE 0.0008147 0.0003910 2.084 0.0372 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 18:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0268609 0.0004991 53.82 <0.0000000000000002 ***
## short_listTRUE -0.0068311 0.0006006 -11.37 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 19:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0129066 0.0003249 39.726 < 0.0000000000000002 ***
## short_listTRUE -0.0016947 0.0003861 -4.389 0.0000114 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 20:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0086065 0.0003458 24.89 <0.0000000000000002 ***
## short_listTRUE 0.0060669 0.0004429 13.70 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 21:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0124514 0.0003124 39.855 < 0.0000000000000002 ***
## short_listTRUE -0.0011159 0.0003980 -2.804 0.00505 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 22:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0127297 0.0003157 40.326 <0.0000000000000002 ***
## short_listTRUE 0.0008318 0.0003993 2.083 0.0372 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 23:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0185756 0.0005551 33.463 < 0.0000000000000002 ***
## short_listTRUE -0.0027946 0.0006233 -4.484 0.00000735 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 24:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0138603 0.0002290 60.516 < 0.0000000000000002 ***
## short_listTRUE 0.0013402 0.0002851 4.702 0.00000259 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 25:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0111248 0.0001346 82.653 < 0.0000000000000002 ***
## short_listTRUE 0.0007890 0.0001684 4.686 0.00000279 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 26:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0098015 0.0002871 34.14 <0.0000000000000002 ***
## short_listTRUE 0.0037726 0.0003834 9.84 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 27:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0103138 0.0002167 47.587 < 0.0000000000000002 ***
## short_listTRUE 0.0009108 0.0002731 3.335 0.000852 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 28:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0111592 0.0004003 27.877 < 0.0000000000000002 ***
## short_listTRUE 0.0028078 0.0005067 5.541 0.0000000302 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 29:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0105108 0.0003591 29.266 <0.0000000000000002 ***
## short_listTRUE 0.0042226 0.0004472 9.442 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 30:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0169430 0.0004391 38.59 <0.0000000000000002 ***
## short_listTRUE 0.0007979 0.0005542 1.44 0.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 31:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0369173 0.0002220 166.3 <0.0000000000000002 ***
## short_listTRUE -0.0068129 0.0002827 -24.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 32:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0100393 0.0001339 74.960 <0.0000000000000002 ***
## short_listTRUE -0.0002587 0.0001601 -1.616 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 33:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0230283 0.0005077 45.36 <0.0000000000000002 ***
## short_listTRUE -0.0059535 0.0006388 -9.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 34:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0136827 0.0004462 30.662 <0.0000000000000002 ***
## short_listTRUE 0.0010979 0.0005552 1.978 0.048 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 35:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0198285 0.0003381 58.65 <0.0000000000000002 ***
## short_listTRUE -0.0061414 0.0003909 -15.71 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 36:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0061167 0.0003208 19.07 <0.0000000000000002 ***
## short_listTRUE 0.0041061 0.0004083 10.06 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 37:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0069657 0.0003196 21.794 < 0.0000000000000002 ***
## short_listTRUE 0.0015000 0.0004215 3.558 0.000373 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 38:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0226435 0.0005276 42.92 <0.0000000000000002 ***
## short_listTRUE -0.0073560 0.0006033 -12.19 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 39:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0205479 0.0006330 32.459 <0.0000000000000002 ***
## short_listTRUE 0.0011686 0.0008248 1.417 0.157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 40:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0139287 0.0003563 39.09 <0.0000000000000002 ***
## short_listTRUE 0.0047449 0.0004501 10.54 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 41:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0107933 0.0001173 92.02 <0.0000000000000002 ***
## short_listTRUE -0.0014800 0.0001338 -11.06 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 42:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0150546 0.0004394 34.258 < 0.0000000000000002 ***
## short_listTRUE -0.0033632 0.0005466 -6.153 0.000000000766 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 43:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0140285 0.0004847 28.944 < 0.0000000000000002 ***
## short_listTRUE 0.0021713 0.0006060 3.583 0.00034 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 44:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0147160 0.0002666 55.195 < 0.0000000000000002 ***
## short_listTRUE 0.0016371 0.0003344 4.896 0.000000981 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 45:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0135446 0.0004531 29.893 < 0.0000000000000002 ***
## short_listTRUE -0.0036865 0.0005326 -6.922 0.00000000000448 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 46:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0067864 0.0003654 18.57 <0.0000000000000002 ***
## short_listTRUE 0.0042905 0.0004437 9.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 47:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0077671 0.0003507 22.14 < 0.0000000000000002 ***
## short_listTRUE 0.0027525 0.0004454 6.18 0.000000000644 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 48:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0225033 0.0005094 44.17 <0.0000000000000002 ***
## short_listTRUE -0.0077118 0.0005846 -13.19 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 49:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0155770 0.0004835 32.215 < 0.0000000000000002 ***
## short_listTRUE -0.0038810 0.0005926 -6.549 0.0000000000582 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 50:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0232448 0.0006509 35.71 <0.0000000000000002 ***
## short_listTRUE -0.0076653 0.0007471 -10.26 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 51:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0105760 0.0003567 29.65 <0.0000000000000002 ***
## short_listTRUE 0.0055916 0.0004514 12.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 52:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01749013 0.00018623 93.915 <0.0000000000000002 ***
## short_listTRUE 0.00001905 0.00022924 0.083 0.934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 53:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0107598 0.0003976 27.063 <0.0000000000000002 ***
## short_listTRUE 0.0003065 0.0004669 0.657 0.511
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 54:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0261048 0.0004279 61.000 <0.0000000000000002 ***
## short_listTRUE -0.0044993 0.0004925 -9.135 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 55:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0101880 0.0001856 54.899 <0.0000000000000002 ***
## short_listTRUE -0.0004804 0.0002437 -1.972 0.0487 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 56:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0114035 0.0003502 32.563 < 0.0000000000000002 ***
## short_listTRUE -0.0015375 0.0004365 -3.522 0.000428 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 57:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0129069 0.0003207 40.243 < 0.0000000000000002 ***
## short_listTRUE 0.0012919 0.0004120 3.136 0.00172 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 58:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0090850 0.0002448 37.113 < 0.0000000000000002 ***
## short_listTRUE 0.0015904 0.0003330 4.775 0.0000018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 59:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0166458 0.0001923 86.552 < 0.0000000000000002 ***
## short_listTRUE 0.0007301 0.0002314 3.155 0.00161 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 60:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0100660 0.0003752 26.831 <0.0000000000000002 ***
## short_listTRUE -0.0002233 0.0004601 -0.485 0.628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 61:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0112650 0.0001136 99.15 <0.0000000000000002 ***
## short_listTRUE 0.0019039 0.0001402 13.58 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 62:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0104588 0.0003939 26.554 <0.0000000000000002 ***
## short_listTRUE -0.0010404 0.0004713 -2.208 0.0273 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 63:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0088039 0.0003593 24.500 < 0.0000000000000002 ***
## short_listTRUE 0.0020095 0.0004632 4.338 0.0000144 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 64:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0207843 0.0002282 91.080 < 0.0000000000000002 ***
## short_listTRUE 0.0017465 0.0003042 5.742 0.00000000942 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 65:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0136841 0.0003857 35.481 < 0.0000000000000002 ***
## short_listTRUE -0.0017372 0.0004613 -3.766 0.000166 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 66:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0132102 0.0004836 27.317 < 0.0000000000000002 ***
## short_listTRUE 0.0037765 0.0005855 6.451 0.000000000112 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 67:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0219813 0.0003095 71.033 < 0.0000000000000002 ***
## short_listTRUE -0.0011535 0.0003847 -2.999 0.00271 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 68:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0193964 0.0001788 108.5 <0.0000000000000002 ***
## short_listTRUE -0.0026870 0.0002297 -11.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 69:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00275135 0.00001988 138.408 <0.0000000000000002 ***
## short_listTRUE -0.00002552 0.00002595 -0.983 0.325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Airoldi, E. M., & Bischof, J. M. (2016). Improving and Evaluating Topic Models and Other Models of Text. Journal of the American Statistical Association, 111(516), 1381–1403. https://doi.org/10.1080/01621459.2015.1051182
Andeweg, R. B., & Thomassen, J. J. (2005). Modes of Political Representation: Toward a New Typology. Legislative Studies Quarterly, 30(4), 507–528. https://doi.org/10.3162/036298005X201653
Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag, D., … Zhu, M. (2013). A Practical Algorithm for Topic Modeling with Provable Guarantees. In S. Dasgupta & D. McAllester (Eds.), Proceedings of the 30th International Conference on Machine Learning (Vol. 28, pp. 280–288). Atlanta, Georgia, USA: PMLR. Retrieved from http://proceedings.mlr.press/v28/arora13.pdf
Audickas, L., Hawkins, O., & Cracknell, R. (2017). UK Election Statistics: 1918-2017 (Briefing Paper No. CBP7529) (p. 89). London: House of Commons Library. Retrieved from http://researchbriefings.parliament.uk/ResearchBriefing/Summary/CBP-7529
Benoit, K. (2018). Quanteda: Quantitative Analysis of Textual Data. https://doi.org/10.5281/zenodo.1004683
Benoit, K., & Matsuo, A. (2018). Spacyr: Wrapper to the ’spaCy’ ’NLP’ Library. Retrieved from http://github.com/quanteda/spacyr
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
Bligh, M., Merolla, J., Schroedel, J. R., & Gonzalez, R. (2010). Finding Her Voice: Hillary Clinton’s Rhetoric in the 2008 Presidential Campaign. Women’s Studies, 39(8), 823–850. https://doi.org/10.1080/00497878.2010.513316
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). Hillsdale, N.J: L. Erlbaum Associates.
Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129–1164. https://doi.org/10.1002/spe.4380211102
Gagolewski, M. (2018). R package stringi: Character string processing facilities. https://doi.org/10.5281/zenodo.1292492
Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(03), 267–297. https://doi.org/10.1093/pan/mps028
Honnibal, M., & Montani, I. (2017). spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To Appear. Retrieved from https://spacy.io
Jones, J. J. (2016). Talk "Like a Man": The Linguistic Styles of Hillary Clinton, 1992-2013. Perspectives on Politics, 14(03), 625–642. https://doi.org/10.1017/S1537592716001092
Kelly, R. (2016). All-women shortlists (Briefing Paper No. 5057) (p. 34). London: House of Commons Library. Retrieved from https://researchbriefings.parliament.uk/ResearchBriefing/Summary/SN05057
Kincaid, J. P., Fishburne, R. P., Rogers, R. L., & Chissom, B. S. (1975). Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel: Fort Belvoir, VA: Defense Technical Information Center. https://doi.org/10.21236/ADA006655
Lee, M., & Mimno, D. (2014). Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1319–1328). Doha, Qatar: Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1138
Newman, M. L., Groom, C. J., Handelman, L. D., & Pennebaker, J. W. (2008). Gender Differences in Language Use: An Analysis of 14,000 Text Samples. Discourse Processes, 45(3), 211–236. https://doi.org/10.1080/01638530802073712
Odell, E. (2018). Hansard Speeches and Sentiment V2.5.1 [dataset]. https://doi.org/10.5281/zenodo.1306964
Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The Development and Psychometric Properties of LIWC2015, 26. Retrieved from https://repositories.lib.utexas.edu/bitstream/handle/2152/31333/LIWC2015_LanguageManual.pdf
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